scholarly journals An evaluation of daily precipitation from atmospheric reanalyses over Australia

2019 ◽  
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
Daily Precipitation ◽  
Transition Probabilities ◽  
Reanalysis Dataset ◽  
Atmospheric Processes ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Regional Reanalysis ◽  
Global Reanalysis ◽  
Better Than

Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, modified Kling-Gupta efficiency), categorical metrics, and other statistics (wet day frequency, transition probabilities and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both point and grid scale of 5 km. BARRA also consistently reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias and modified Kling-Gupta efficiency (KGE') indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms AWAP in terms of KGE' score and the components of the KGE' score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.

scholarly journals An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia

2019 ◽  
Vol 23 (8) ◽  
pp. 3387-3403 ◽  
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
Daily Precipitation ◽  
Transition Probabilities ◽  
Reanalysis Dataset ◽  
Australian Water ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Regional Reanalysis ◽  
Global Reanalysis ◽  
Better Than

Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE′ score and the components of the KGE′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.

scholarly journals Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 687
Author(s):  
Salman Sakib ◽  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Stage Iv ◽  
Tropical Storm ◽  
Average Correlation ◽  
Acceptable Range ◽  
Precipitation Estimates ◽  
Overall Performance ◽  
Early Late ◽  
Statistical Metrics ◽  
Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

Spatial and temporal evaluation of multiple gridded precipitation datasets over complex topography and variable climate of Turkey

2021 ◽  
Author(s):  
Gokcen Uysal ◽  
Hamed Hafizi ◽  
Ali Arda Sorman
Keyword(s):  
Poor Performance ◽  
Temporal Consistency ◽  
Precipitation Data ◽  
Precipitation Estimates ◽  
Dry Seasons ◽  
Spatio Temporal ◽  
Autumn And Winter ◽  
Development And Management ◽  
Precipitation Events ◽  
Better Than

<p>Evaluation of problems related to water resources development and management require accurate precipitation estimates. Although ground-based stations provide direct physical measurement of precipitation, the accuracy of gauge-based precipitation data in terms of quality and spatial pattern may still be controversial. On the other hand, Gridded Precipitation Datasets (GPDs) provide high spatial and temporal precipitation estimates. GPDs are continuously changing with the improving technology and updating of retrospective algorithms, but they still need to be assessed over different regions both in space and time before being used for hydro-climatic studies. This study attempts to evaluate the spatio-temporal consistency of 13 different GPDs (CPCv1, MSWEPv2.2, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3b42v07, TMPA-3b42RTv07, PERSIANN-CDR, PERSIANN-CCS and PERSIANN) over Turkey which is a country characterized by diverse climate and complex terrain. The evaluation is performed for daily and monthly time scales considering the entire period of 2015-2019 as well as seasonal (spring, summer, autumn and winter) variability. Precipitation data from 130 stations are provided as reference data for point-to-grid comparison of GPDs. The modified Kling Gupta Efficiency (KGE) is selected for qualitative analysis whereas the Hanssen–Kuipers Score (HKS) is used to identify the ability of GPDs for capturing various precipitation events. The Probability Density Function (PDF) is selected to evaluate the intensity frequency of 13 GPDs for individual daily-based precipitation events. The results indicate that all GPDs have a median KGE performance ranging between -0.11 and 0.53 for daily precipitation while their performance increases in the monthly case (median KGE from 0.16 to 0.82). Gauge-corrected GPDs exhibit slightly better results over the uncorrected datasets in comparison with ground observations. GPDs from multi-source merging perform better than only satellite-based and reanalysis precipitation datasets. Among uncorrected GPDs, ERA5 and CHIRPv2.0 perform better while PERSIANN perform worse in all conditions. MSWEPv2.2 suffers from high-altitude conditions during winter and CHIRPSv2.0 shows poor performance during dry seasons. On the overall, MSWEPv2.2 performs better than CHIRPSv2.0 during daily/monthly, while CHIRPv2.0 performs better than CHIRPSv2.0 for daily time scale.</p>

scholarly journals Evaluation of Global Satellite Mapping of Precipitation Project Daily Precipitation Estimates over the Chinese Mainland

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhuoqi Chen ◽  
Yaxin Qin ◽  
Yan Shen ◽  
Shupeng Zhang
Keyword(s):  
Snow Cover ◽  
Daily Precipitation ◽  
Chinese Mainland ◽  
Rainfall Analysis ◽  
Sparse Vegetation ◽  
Vegetation Fraction ◽  
The Chinese Mainland ◽  
Precipitation Estimates ◽  
Satellite Mapping

Two versions of Global Satellite Mapping of Precipitation (GSMaP) products (GSMaP-V4 and GSMaP-V5) are validated both in a single grid scale and in contiguous China by comparing to gauge-based rainfall analysis dataset. GSMaP products can capture spatial patterns and magnitude of rainfall in daily mean precipitation. They perform better in summer than in winter over the Chinese Mainland. They also have better estimation over the southeast than over the northwest of the Chinese Mainland. An apparent system underestimate is detected in both GSMaP products. The underestimation existing in the GSMaP-V4 has been largely improved in GSMaP-V5. The impacts of snow cover and vegetation fraction are also investigated. The result indicates that snow cover deeply impacts the POD and FAR of GSMaP products. NDVI may result in overestimated precipitation in sparse vegetation regions. These results implicate that it is useful to use some auxiliary data from other sensors (e.g., MODIS) to improve the quality of precipitation product.

scholarly journals Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

2020 ◽  
Vol 24 (6) ◽  
pp. 2951-2962
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
High Resolution ◽  
Spatial Scale ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
Daily Rainfall ◽  
Spatial Displacement ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
Spatial Evaluation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

scholarly journals The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events

2021 ◽  
Author(s):  
Antonio Giordani ◽  
Ines Cerenzia ◽  
Tiziana Paccagnella ◽  
Silvana Di Sabatino
Keyword(s):  
High Resolution ◽  
Dynamical Downscaling ◽  
Temporal Frequency ◽  
Severe Weather ◽  
Added Value ◽  
Limited Area ◽  
Reanalysis Dataset ◽  
Weather Events ◽  
Regional Reanalysis ◽  
Global Reanalysis

<p>In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.</p><p>The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.</p>

scholarly journals Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1430
Author(s):  
Jean Vega-Durán ◽  
Brigitte Escalante-Castro ◽  
Fausto A. Canales ◽  
Guillermo J. Acuña ◽  
Bartosz Kaźmierczak
Keyword(s):  
Performance Metrics ◽  
Spatial Scales ◽  
Reanalysis Dataset ◽  
Rain Gauges ◽  
Ground Measurement ◽  
Average Precipitation ◽  
Precipitation Estimates ◽  
Thiessen Polygons ◽  
Areal Precipitation ◽  
Global Reanalysis

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

scholarly journals Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

2019 ◽  
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
High Resolution ◽  
Spatial Scale ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
Daily Rainfall ◽  
Spatial Displacement ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
Spatial Evaluation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the Fractions Skill Score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA are found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75 % quantile (90 % quantile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

Kepuasan Masyarakat Terhadap Pelayanan Kelurahan Setelah Pemekaran Di Tangerang Selatan

Liquidity ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 151-159
Author(s):  
Pitri Yandri
Keyword(s):  
Public Perception ◽  
Service Personnel ◽  
Urban Services ◽  
Hospitality Workers ◽  
Care Workers ◽  
Before And After ◽  
Cartesian Diagram ◽  
Better Than

The purpose of this study is (1) to analyze public perception on urban services before and after the expansion of the region, (2) analyze the level of people's satisfaction with urban services, and (3) analyze the determinants of the variables that determine what level of people's satisfaction urban services. This study concluded that first, after the expansion, the quality of urban services in South Tangerang City is better than before. Secondly, however, public satisfaction with the services only reached 48.53% (poor scale). Third, by using a Cartesian Diagram, the second priority that must be addressed are: (1) clarity of service personnel, (2) the discipline of service personnel, (3) responsibility for care workers; (4) the speed of service, (5) the ability of officers services, (6) obtain justice services, and (7) the courtesy and hospitality workers.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

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