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

2019 ◽  
Vol 11 (23) ◽  
pp. 2840 ◽  
Author(s):  
Mohamed Shawky ◽  
Adel Moussa ◽  
Quazi K Hassan ◽  
Naser El-Sheimy
Keyword(s):  
Flash Flood ◽  
Heavy Rain ◽  
Arid Environment ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Spatial And Temporal Patterns ◽  
Light Rain ◽  
Precipitation Estimates ◽  
Precipitation Records

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.

scholarly journals Evaluation of a near-real time NEXRAD DSP product in evolution of heavy rain events on the Upper Guadalupe River Basin, Texas

2012 ◽  
Vol 15 (2) ◽  
pp. 464-485 ◽  
Author(s):  
Xianwei Wang ◽  
Hongjie Xie ◽  
Newfel Mazari ◽  
Jon Zeitler ◽  
Hatim Sharif ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Flash Flood ◽  
Heavy Rain ◽  
Accurate Information ◽  
Total Rainfall ◽  
Light Rain ◽  
Guadalupe River ◽  
Rain Events ◽  
Resource Budget

This study evaluates the Next Generation Weather Radar (NEXRAD) Digital Storm-Total Precipitation product (DSP) by analyzing 30 rain events on the Upper Guadalupe River Basin, Texas, from September 2006 to May 2007. The DSP product provides relatively accurate information on the evolution of rain events at high spatial and temporal resolutions in near-real time. This is particularly important for rainfall estimation of heavy rain events and flash flood forecasting. The DSP's accuracy is comparable to the other NEXRAD product MPE (multisensor precipitation estimator, at hourly resolution and 4 km grid spacing) at both hourly and event total scales for some heavy rain events, although the DSP is inferior to the MPE product for total rainfall of all 30 rain events analyzed, especially for light rain events. The DSP product shows the best agreement with gauges at ranges of 50–150 km from the radar (with mean absolute estimation bias (MAEB) of +15–22% for total rainfall of 30 rain events), while underestimating precipitation at both close ranges (<30 km) and far ranges (>180 km). The DSP product also tends to underestimate (overestimate) precipitation during event growth (dissipation). However, the total rainfall estimate for all rain events over a long period from DSP shows range dependence and is not recommended for calculation of water resource budget.

scholarly journals Evaluation of CMPA precipitation estimate in the evolution of typhoon-related storm rainfall in Guangdong, China

2016 ◽  
Vol 18 (6) ◽  
pp. 1055-1068 ◽  
Author(s):  
Dashan Wang ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dagang Wang ◽  
Huabing Huang ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Spatiotemporal Evolution ◽  
Light Rain ◽  
Storm Rainfall ◽  
Climate Prediction Center ◽  
Rain Gauge Network

The merged precipitation data of Climate Prediction Center Morphing Technique and gauge observations (CMPA) generated for continental China has relatively high spatial and temporal resolution (hourly and 0.1°), while few studies have applied it to investigate the typhoon-related extreme rainfall. This study evaluates the CMPA estimate in quantifying the typhoon-related extreme rainfall using a dense rain gauge network in Guangdong Province, China. The results show that the event-total precipitation from CMPA is generally in agreement with gauges by relative bias (RB) of 2.62, 10.74 and 0.63% and correlation coefficients (CCs) of 0.76, 0.86 and 0.91 for typhoon Utor, Usagi and Linfa events, respectively. At the hourly scale, CMPA underestimates the occurrence of light rain (<1 mm/h) and heavy rain (>16 mm/h), while overestimates the occurrence of moderate rain. CMPA shows high probability of detection (POD = 0.93), relatively large false alarm ratio (FAR = 0.22) and small missing ratio (0.07). CMPA captures the spatial patterns of typhoon-related rain depth, and is in agreement with the spatiotemporal evolution of hourly gauge observations by CC from 0.93 to 0.99. In addition, cautiousness should be taken when applying it in hydrologic modeling for flooding forecasting since CMPA underestimates heavy rain (>16 mm/h).

scholarly journals Comparative analysis of TMPA and IMERG precipitation datasets in the arid environment of El- Qaa Plain, Sinai

2021 ◽  
Author(s):  
Mona Morsy ◽  
Thomas Scholten ◽  
Silas Michaelides ◽  
Erik Borg ◽  
Youssef Sherief ◽  
...  
Keyword(s):  
Light Intensity ◽  
Arid Environment ◽  
Rain Gauge ◽  
Data Set ◽  
Light Rain ◽  
Rain Gauges ◽  
Data Source ◽  
Local Water ◽  
Rain Events

<p>The replenishment of aquifers depends mainly on precipitation rates, which is of vital 19 importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in Sinai 20 Peninsula is such a region which experiences a constant population growth. The local water budget 21 equilibrium is negatively affected by relatively frequent light rain events. This study compares the 22 performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The 23 dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial 24 resolution TMPA (TRMM Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-25 satellitE Retrievals for GPM) data were retrieved and analyzed, employing appropriate statistical 26 metrics. The best-performing data set was determined as the data source capable to most accurately 27 bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events 28 and rarer heavy-intensity events. With light-intensity events the corresponding satellite-based data 29 sets differ the least and correlate more, while the greatest differences and weakest correlations are 30 noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges 31 during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior 32 performance than TMPA, in all rainfall intensities.</p>

Validation of Polarimetric Hail Detection

2006 ◽  
Vol 21 (5) ◽  
pp. 839-850 ◽  
Author(s):  
Pamela L. Heinselman ◽  
Alexander V. Ryzhkov
Keyword(s):  
Confidence Level ◽  
Statistical Significance ◽  
Ground Truth ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Superior Performance ◽  
False Alarms ◽  
Statistical Significance Testing ◽  
Light Rain

Abstract This study describes, illustrates, and validates hail detection by a simplified version of the National Severe Storms Laboratory’s fuzzy logic polarimetric hydrometeor classification algorithm (HCA). The HCA uses four radar variables: reflectivity, differential reflectivity, cross-correlation coefficient, and “reflectivity texture” to classify echoes as rain mixed with hail, ground clutter–anomalous propagation, biological scatterers (insects, birds, and bats), big drops, light rain, moderate rain, and heavy rain. Diagnostic capabilities of HCA, such as detection of hail, are illustrated for a variety of storm environments using polarimetric radar data collected mostly during the Joint Polarimetric Experiment (JPOLE; 28 April–13 June 2003). Hail classification with the HCA is validated using 47 rain and hail reports collected by storm-intercept teams during JPOLE. For comparison purposes, probability of hail output from the Next-Generation Weather Radar Hail Detection Algorithm (HDA) is validated using the same ground truth. The anticipated polarimetric upgrade of the Weather Surveillance Radar-1988 Doppler network drives this direct comparison of performance. For the four examined cases, contingency table statistics show that the HCA outperforms the HDA. The superior performance of the HCA results primary from the algorithm’s lack of false alarms compared to the HDA. Statistical significance testing via bootstrapping indicates that differences in the probability of detection and critical success index between the algorithms are statistically significant at the 95% confidence level, whereas differences in the false alarm rate and Heidke skill score are statistically significant at the 90% confidence level.

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

2018 ◽  
Vol 13 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Muhammad Mohsan ◽  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Heavy Rain ◽  
Probability Of Detection ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Observation Network ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Statistical Metrics

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

DISTRIBUIÇÃO ESPAÇO-TEMPORAL DOS DESASTRES HIDROMETEOROLÓGICOS NA MICRORREGIÃO PLUVIOMÉTRICAMENTE HOMOGÊNEA DO LITORAL PARAIBANO

2019 ◽  
Vol 21 (2) ◽  
pp. 467-476
Author(s):  
Wanessa Janinne Eloy Da Silva ◽  
Maressa Oliveira Lopes Araújo ◽  
Marcelo De Oliveira Moura
Keyword(s):  
Flash Flood ◽  
Temporal Distribution ◽  
Heavy Rain ◽  
Emergency Situations

O presente trabalho tem por objetivo analisar a distribuição espaço-temporal dos reconhecimentos de Situação de Emergência associados à dinâmica hidrometeorológica na microrregião pluviometricamente homogênea do Litoral paraibano, durante o período de 2003 a 2016. Para isso, foram utilizados dados adquiridos no site do Ministério da Integração Nacional, encontrados na página da Secretaria Nacional de Proteção e Defesa Civil, conforme reconhecimentos disponibilizados através de portarias. Como resultados principais, constatou-se um total de 29 reconhecimentos, em que 51,7% corresponde a enchentes; 20,7% a chuvas intensas; 24,2% correspondente a enxurradas e 3,4% a inundações. Considera-se que os resultados obtidos tiveram um cunho mais descritivo, necessitando assim de estudos mais avançados sobre a temática.Palavras chave: Litoral Paraibano, desastres hidrometeorológicos, situação de emergência. ABSTRACTThe present work has for objective analyze the space-temporal distribution of the emergency situations recognizements associated to the hydrometeorological dynamic on the pluviometrically homogenius microregion of the coast of Paraíba, during the period of 2003 to 2016. For that, data were used acquired from the Ministério da Integração Nacional’s site, found on the Secretaria Nacional de Proteção e Defesa Civil’s page, conform available recognizements through ordinances. As main results, a total of 29 recognizements were found, in which 51,7% corresponds to floods; 20,7% to heavy rain; 24,2% corresponding to flash flood and 3,4% to inundations. It’s considered that the obtained results have a descriptive label, needing then advanced studies about the theme.Keywords: Coast of Paraiba, hydrometeorlogical disasters, emergency situations. RESUMENEste documento tiene como objetivo analizar la distribución espacio-temporal de los reconocimientos de situaciones de emergencia com la dinâmica hidrometeorológica em la microrregión de lluvia homogénea de la costa paraibana, de 2003 a 2016. Para este propósito, se utilizaron los datos adquiridos del sitio web del Ministerio de Salud. Integración nacional, que se encuentra en la página de la Secretaría Nacional de Protección y Defensa Civil, como agradecimientos disponibles a través de ordenanzas. Como resultados principales, hubo un total de 29 reconocimientos, de los cuales el 51.7% correspondió a inundaciones; 20.7% a fuertes lluvias; 24.2% correspondientes a enxurradas y 3.4% a inundaciones. Se considera que los resultados obtenidos tuvieron una naturaleza más descriptiva, por lo que requirieron estúdios más avanzados sobre el tema.Palabras clave: Costa de Paraiba, desastres hidrometeorológicos, situación de emergencia.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

scholarly journals Microphysical Characteristics of Rainfall Observed by a 2DVD Disdrometer during Different Seasons in Beijing, China

2021 ◽  
Vol 13 (12) ◽  
pp. 2303
Author(s):  
Li Luo ◽  
Jia Guo ◽  
Haonan Chen ◽  
Meilin Yang ◽  
Mingxuan Chen ◽  
...  
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Occurrence Frequency ◽  
Total Rainfall ◽  
Axis Ratio ◽  
Seasonal Differences ◽  
Mean Values ◽  
Light Rain ◽  
Beijing China ◽  
Maximum Occurrence

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13°N, 116.62°E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (?) relationship (Z = ?R?) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.

scholarly journals Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1061
Author(s):  
Thanh Thi Luong ◽  
Judith Pöschmann ◽  
Rico Kronenberg ◽  
Christian Bernhofer
Keyword(s):  
Soil Moisture ◽  
Flash Flood ◽  
Rainfall Threshold ◽  
Probability Of Detection ◽  
Water Levels ◽  
Critical Success ◽  
Flood Warning ◽  
Critical Discharge ◽  
Critical Success Index

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

scholarly journals Impact of Topography and Rainfall Intensity on the Accuracy of IMERG Precipitation Estimates in an Arid Region

2020 ◽  
Vol 13 (1) ◽  
pp. 13
Author(s):  
Mohammed T. Mahmoud ◽  
Safa A. Mohammed ◽  
Mohamed A. Hamouda ◽  
Mohamed M. Mohamed
Keyword(s):  
Rainfall Intensity ◽  
Poor Performance ◽  
Rain Gauge ◽  
Detection Accuracy ◽  
Rain Gauge Data ◽  
Light Rain ◽  
Mountainous Regions ◽  
Data Gaps ◽  
Precipitation Estimates ◽  
Evaluation Techniques

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions.

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