scholarly journals Objective Evaluation of Satellite Precipitation Datasets for Heavy Precipitation Events Caused by Typhoons in the Philippines

2021 ◽  
Author(s):  
Putu Aryastana ◽  
Chian-Yi Liu ◽  
Ben Jong-Dao Jou ◽  
Esperanza Cayanan ◽  
Jason Pajimola Punay
Keyword(s):  
Heavy Rainfall ◽  
Heavy Precipitation ◽  
The Philippines ◽  
Superior Performance ◽  
Rainfall Events ◽  
Satellite Precipitation ◽  
Remote Areas ◽  
Wind Speeds ◽  
Precipitation Events ◽  
Strong Ability

Abstract Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground-based radar, because these instruments have an uneven distribution in remote areas. Satellite precipitation datasets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. This study performed subdaily (3-hour) assessments of SPDs (i.e., the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [IMERG], Global Satellite Mapping of Precipitation [GSMaP], and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks datasets) during five typhoon-related heavy precipitation events in the Philippines between 2016 and 2018. The aforementioned assessments were performed through a point-to-grid comparison by using continuous and volumetric statistical validation indices for the 34-knot wind radii of the typhoons, rainfall intensity, the terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall during five typhoon events, whereas the GSMaP exhibited high agreement during peak rainfall. All the SPDs tended to overestimate rainfall during light to moderate rainfall events and underestimate rainfall during heavy to extreme events. The IMERG exhibited a strong ability to detect moderate rainfall events (5–15 mm/3 hours), whereas the GSMaP exhibited superior performance in detecting heavy to extreme rainfall events (15–25, 25–50, and >50 mm/3 hours). The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas the IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. A strong ability to detect heavy rainfall events for different wind speeds in the western and eastern parts of the mountainous region of Luzon were found for the GSMap and IMERG, respectively. This study demonstrated that the IMERG and GSMaP datasets exhibit promising performance in detecting heavy precipitation caused by typhoon events.

scholarly journals Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020)

2021 ◽  
Author(s):  
Ewelina Walawender ◽  
Katharina Lengfeld ◽  
Tanja Winterrath ◽  
Elmar Weigl ◽  
Andreas Becker
Keyword(s):  
Heavy Rainfall ◽  
Open Data ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Rainfall Events ◽  
Antecedent Precipitation ◽  
Hourly Data ◽  
Spatio Temporal ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.</p><p>With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.</p><p>Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.</p><p>The Catalogue is freely available via DWD’s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience. </p><p>We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).</p>

scholarly journals Anomalous Weather Patterns in Relation to Heavy Precipitation Events in Japan during the Baiu Season

2015 ◽  
Vol 16 (2) ◽  
pp. 688-701 ◽  
Author(s):  
Masamichi Ohba ◽  
Shinji Kadokura ◽  
Yoshikatsu Yoshida ◽  
Daisuke Nohara ◽  
Yasushi Toyoda
Keyword(s):  
Time Scales ◽  
Heavy Rainfall ◽  
Southern Oscillation ◽  
Potential Temperature ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Weather Patterns ◽  
Extreme Precipitation Events ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract Anomalous weather patterns (WPs) in relation to heavy precipitation events during the baiu season in Japan are investigated using a nonlinear classification technique known as the self-organizing map (SOM). The analysis is performed on daily time scales using the Japanese 55-year Reanalysis Project (JRA-55) to determine the role of circulation and atmospheric moisture on extreme events and to investigate interannual and interdecadal variations for possible linkages with global-scale climate variability. SOM is simultaneously employed on four atmospheric variables over East Asia that are related to baiu front variability, whereby anomalous WPs that dominated during the 1958–2011 period are obtained. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of heavy precipitation events. Each WP is associated with regional variations in the probability of extreme precipitation events. On interannual time scales, El Niño–Southern Oscillation (ENSO) affects the frequency of the WPs in relation to the heavy rainfall events. The warm phase of ENSO results in an increased frequency of a WP that provides a southwesterly intrusion of high equivalent potential temperature at low levels, while the cold phase provides southeastern intrusion. In addition, the results of this analysis suggest that interdecadal variability of frequency for heavy rainfall events corresponds to changes in frequency distributions of WPs and are not due to one particular WP.

Atmospheric Rivers in Association with Summer Heavy Rainfall over the Yangtze Plain on Sub-seasonal Time Scale

2021 ◽  
Author(s):  
Ping Liang ◽  
Guangtao Dong ◽  
Huqiang Zhang ◽  
Mei Zhao ◽  
Yue Ma
Keyword(s):  
Time Scales ◽  
Lead Time ◽  
Heavy Rainfall ◽  
East Asian ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Atmospheric Rivers ◽  
The Tropics ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>Atmospheric Rivers (ARs), referring to long and narrow bands of enhanced water vapor transport, mainly from the tropics into the mid-latitudes in the low atmosphere. They often contribute to heavy rainfall generations outside the tropics. However, there is a lack of such AR studies in East Asia and it is still unclear how ARs act on different time scales during the boreal summer when frequent heavy precipitation events take place over the region. In this study, climatological ARs and their evolutions on both synoptic and sub-seasonal time scales associated with heavy rainfall events over the Yangtze Plain in China are investigated. Furthermore, its predictability is assessed by examining hindcast skills from an operational coupled seasonal forecast model. Results show that ARs embedded within the South Asian monsoon and Somali cross-equatorial flow provide a favorable background for steady moisture supply of summer rainfall into East Asia. We can call this favorable background as a climatological East Asian AR which has close connections with seasonal cycle and climatological intra-seasonal oscillation (CISO) of rainfall in the Yangtze Plain during its Meiyu season. The East Asian AR is also influenced by anomalous anti-cyclonic circulations over the tropical West Pacific when heavy rainfall events occur over the Yangtze Plain. Different from orography-induced precipitation, ARs leading to heavy rainfall over the Yangtze Plain are linked with the intrusions of cold air from its north. The major source of ARs responsible for heavy precipitation events over the Yangtze Plain appears to originate from tropical West Pacific on both synoptic and sub-seasonal time scales. By analyzing 23-yr hindcasts for May-June-July with start date of 1 May, we show that the current operational coupled seasonal forecast system of the Australian Bureau of Meteorology (named as ACCESS-S1) has skillful rainfall forecasts at lead-time of 0 month (i.e. forecasting May monthly mean with initial conditions on 1 May), but the skill degrades significantly at longer lead time. Nevertheless, the model shows skills in predicting the variations of low-level moisture transport affecting the Yangtze River at longer lead time, suggesting that the ARs influencing summer monsoon rainfall in the East Asian region are likely to be more predictable than rainfall itself. This provides a potential of utilizing the skill from the coupled forecast system in predicting ARs to guide its rainfall forecasts in the East Asian summer season at longer lead time.</p>

Assessment of GPM and TRMM Satellite Precipitation Products, and their application for Flood Simulations at Daily Scale in a sparsely gauged watershed;Case of Ghdat basin (High Atlas, Morocco).

2021 ◽  
Author(s):  
Myriam Benkirane ◽  
Nour-Eddine Laftouhi ◽  
Said Khabba ◽  
Bouabid El Mansouri
Keyword(s):  
Hydrological Model ◽  
Annual Rainfall ◽  
Spatial And Temporal Variation ◽  
Precipitation Data ◽  
Rainfall Events ◽  
High Atlas ◽  
Satellite Precipitation ◽  
Daily Scale ◽  
Semi Arid ◽  
Precipitation Events

<p>Accurate measurement of precipitation is very important for flood forecasting, hydrological modeling, and estimation of the water balance of any basin. The lack of a weather monitoring network is an obstacle to the accurate measurement of precipitation.</p><p>In most of the Moroccan High Atlas Mountains regions, ground observation stations are still unreliable and difficult to access due to several parameters, such as a large spatial and temporal variation of rainfall and ruggedness of topography, which lead to irregularity and scarcity of measuring stations. This area is characterized by arid and semi-arid climates where generally occurred a few rainy days but have experienced significant flash floods.</p><p>Consequently, floods are causing extended damages to the population and infrastructures every year. However, research on hydrological processes is limited due to the irregularity of the gauge station network and the large number of gaps frequently observed in the rainfall and runoff data acquired from the gauge stations. Remote sensing precipitation data with high spatial and temporal resolution are a potential alternative to gauged precipitation data.</p><p>This study evaluates the performance of the two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA) and the Integrated Multi-satellite Retrievals for GPM (IMERG V06) (SPPs) to observed rainfall, at different time scales (daily, monthly, and annual) from 1 September 2000 to 31 August 2017 over the Ghdat watershed, with different statistical indices and hydrological assessment, to evaluate the reliability of these (SPPs) data to reproduce rainfall events by implementing them in a hydrological model, to determine their ability to detect all types of rainfall events.</p><p>Daily, monthly, and annual rainfall measurements were validated using widely used statistical measures (CC, RMSE, MAE, Bias, Nash, POD, FAR, FBI and ETS).</p><p>The results showed that: (1) The correlation between satellite precipitation data and rainfall precipitation demonstrated a high correlation on all daily, monthly, and annual scales. (2) The product (TRMM 3B42V7) exhibits better quality in terms of correlation on the monthly and annual scale, while the (GPM IMERG V06) product shows a high correlation on the daily scale compared to the measurements of the gauges. (3) The (GPM IMERG V06) product has better performance regarding the precipitation detection capability, compared to the (TRMM 3B42V7) product which could detect only tiny precipitation events, but not able to capture moderate or strong precipitation events. (4) Flood events can be simulated with the hydrological model using both observed precipitation data and satellite data with the Nash – Sutcliffe model efficiency coefficient (NSE) ranging from 0.65 to 0.90.</p><p>According to the results of this study, we concluded that (TRMM 3B42V7) and (GPM IMERG V06) satellite precipitation products can be used for flood modeling and water resource management, particularly in the semi-arid and Mediterranean region.</p>

scholarly journals Oceanic Forcing on Interannual Variability of Sahel Heavy and Moderate Daily Rainfall

2019 ◽  
Vol 20 (3) ◽  
pp. 397-410 ◽  
Author(s):  
M. Diakhaté ◽  
B. Rodríguez-Fonseca ◽  
I. Gómara ◽  
E. Mohino ◽  
A. L. Dieng ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Heavy Precipitation ◽  
Equatorial Atlantic ◽  
Rainfall Events ◽  
Low Level ◽  
Oceanic Forcing ◽  
Sst Anomalies ◽  
Precipitation Events

Abstract This article analyzes SST remote forcing on the interannual variability of Sahel summer (June–September) moderate (below 75th percentile) and heavy (above 75th percentile) daily precipitation events during the period 1981–2016. Evidence is given that interannual variability of these events is markedly different. The occurrence of moderate daily rainfall events appears to be enhanced by positive SST anomalies over the tropical North Atlantic and Mediterranean, which act to increase low-level moisture advection toward the Sahel from the equatorial and north tropical Atlantic (the opposite holds for negative SSTs anomalies). In contrast, heavy and extreme daily rainfall events seem to be linked to El Niño–Southern Oscillation (ENSO) and Mediterranean variability. Under La Niña conditions and a warmer Mediterranean, vertical atmospheric instability is increased over the Sahel and low-level moisture supply from the equatorial Atlantic is enhanced over the area (the reverse is found for opposite-sign SST anomalies). Further evidence suggests that interannual variability of Sahel rainfall is mainly dominated by the extreme events. These results have implications for seasonal forecasting of Sahel moderate and heavy precipitation events based on SST predictors, as significant predictability is found from 1 to 4 months in advance.

scholarly journals Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product

2013 ◽  
Vol 1 (6) ◽  
pp. 6979-7014
Author(s):  
I. Yucel ◽  
A. Onen
Keyword(s):  
Data Assimilation ◽  
Extreme Precipitation ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Hydrologic Model ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the West Black Sea Region of Turkey. Precipitations derived from WRF model with and without three-dimensional variational (3-DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (4 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and in some extent the spatial distribution and magnitude of the heavy rainfall events wheras MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of 3-DVAR scheme. Direct impact of data assimilation on WRF precipitation reached to 12% and at some points there exists quantitative match for heavy rainfall events, which are critical for hydrological forecast.

scholarly journals Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

2012 ◽  
Vol 13 (6) ◽  
pp. 1760-1783 ◽  
Author(s):  
Vera Thiemig ◽  
Rodrigo Rojas ◽  
Mauricio Zambrano-Bigiarini ◽  
Vincenzo Levizzani ◽  
Ad De Roo
Keyword(s):  
Heavy Rainfall ◽  
River Basins ◽  
Superior Performance ◽  
Rainfall Estimation ◽  
Monthly Basis ◽  
Rainfall Events ◽  
Mountainous Regions ◽  
Heavy Rainfall Events ◽  
Trmm 3B42 ◽  
Rainy Days

Abstract Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.

scholarly journals Assessment of Satellite-Derived Precipitation Products for the Beijing Region

2018 ◽  
Vol 10 (12) ◽  
pp. 1914 ◽  
Author(s):  
Meifang Ren ◽  
Zongxue Xu ◽  
Bo Pang ◽  
Wenfeng Liu ◽  
Jiangtao Liu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Absolute Difference ◽  
Monthly Precipitation ◽  
Temporal Scales ◽  
Meteorological Forcing ◽  
Satellite Precipitation ◽  
Climate Prediction Center ◽  
Precipitation Events

Performance of four satellite precipitation products, namely, the China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing technique (CMORPH), as well as 3B42 calibrated and 3B42-RT dataset, which are derived from the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA), were evaluated from daily to annual temporal scales over Beijing, using observations from 36 ground meteorological stations. Five statistical properties and three categorical metrics were used to test the results. The assessment showed that all four satellite precipitation products captured the temporal variability of precipitation. Although four satellite precipitation products captured the trend of more precipitation in the northeastern regions, all four products showed different distribution from the observations for 2001–2015 over Beijing. All precipitation products tended to overestimate moderate precipitation events and underestimate heavy precipitation events over Beijing, except for 3B42RT, which tended to overestimate most precipitation events. By comparison, the CMFD performed better than the CMORPH, 3B42 calibrated, and 3B42-RT datasets, having the higher correlation coefficient and low root mean squared difference, and mean absolute difference at all temporal scales. The average correlation coefficient of the CMFD, CMORPH, 3B42 calibrated, and 3B42-RT products for all 36 stations were 0.70, 0.60, 0.59, and 0.54 for daily precipitation and 0.78, 0.32, 0.74, and 0.44 for monthly precipitation. Overall, the CMFD was the most reliable for the Beijing region.

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