Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China

2018 ◽  
Vol 135 (3-4) ◽  
pp. 877-891 ◽  
Author(s):  
Sheng Chen ◽  
Junjun Hu ◽  
Asi Zhang ◽  
Chao Min ◽  
Chaoying Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Northern China ◽  
Precipitation Estimates ◽  
Precipitation Events ◽  
Satellite Mapping

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Evaluation of Numerical Weather Model–Based Satellite Precipitation Adjustment in Tropical Mountainous Regions

2019 ◽  
Vol 20 (3) ◽  
pp. 431-445 ◽  
Author(s):  
Xinxuan Zhang ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
Satellite Precipitation ◽  
Numerical Weather ◽  
Mountainous Regions ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Precipitation Estimates ◽  
Daily Scale

Abstract The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-time and gauge-adjusted satellite products as well as the WRF simulation were evaluated by independent gauge networks at daily scale and event total scale. Results show that the near-real-time precipitation products exhibited severe underestimation relative to the gauge observations over the three tropical mountainous regions. The underestimation tended to be larger for higher rainfall accumulations. The WRF-based satellite adjustment provided considerable improvements to the near-real-time CMORPH and GSMaP products. Moreover, error metrics show that WRF-adjusted satellite products outperformed the gauge-adjusted counterparts for most of the events. The effectiveness of WRF-based satellite adjustment varied with events of different physical processes. Thus, the technique applied on satellite precipitation estimates of these events may exhibit inconsistencies in the bias correction.

scholarly journals Tuning Extreme NEXRAD and CMORPH Precipitation Estimates

2014 ◽  
Vol 15 (3) ◽  
pp. 1070-1077 ◽  
Author(s):  
Jonathan Woody ◽  
Robert Lund ◽  
Mekonnen Gebremichael
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Heavy Precipitation ◽  
Generalized Pareto ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Alternative Sources ◽  
Climate Prediction Center ◽  
Adequate Ground ◽  
Precipitation Events

Abstract High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.

scholarly journals Evaluation of High-Resolution Satellite-Based Real-Time and Post-Real-Time Precipitation Estimates during 2010 Extreme Flood Event in Swat River Basin, Hindukush Region

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Naveed Anjum ◽  
Yongjian Ding ◽  
Donghui Shangguan ◽  
Muhammad Wajid Ijaz ◽  
Shiqiang Zhang
Keyword(s):  
Real Time ◽  
Correlation Coefficient ◽  
Spatial Correlation ◽  
River Basin ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
Extreme Precipitation Events ◽  
Extreme Flood ◽  
Precipitation Estimates ◽  
Swat River

Satellite-based real-time and post-real-time precipitation estimates of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) were evaluated during an extreme heavy precipitation event (on 28–30 July 2010) over Swat River Basin and adjacent areas in Hindukush Region. Observations of 15 rain gauging stations were used for the evaluation of TMPA products. Results showed that the spatial pattern of precipitation in the event was generally captured by post-real-time product (3B42V7) but misplaced by real-time product (3B42RT), witnessed by a high spatial correlation coefficient for 3B42V7 (CC = 0.87) and low spatial correlation coefficient for 3B42RT (CC = 0.20). The temporal variation of the storm precipitation was not well captured by both TMPA products. 3B42V7 product underestimated the storm accumulated precipitation by 32.15%, while underestimation by 3B42RT was 66.73%. Based on the findings of this study, we suggest that the latest TMPA-based precipitation products, 3B42RT and 3B42V7, might not be able to perform well during extreme precipitation events, particularly in complex terrain regions like Hindukush Mountains. Therefore, cautions should be considered while using 3B42RT and 3B42V7 as input data source for the modelling, forecasting, and monitoring of floods and potential landslides in Hindukush Region.

Statistics of 20 years of heavy precipitation events in Germany from radar data

2021 ◽  
Author(s):  
Katharina Lengfeld ◽  
Ewelina Walawender ◽  
Tanja Winterrath ◽  
Elmar Weigl ◽  
Andreas Becker
Keyword(s):  
Temporal Structure ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Easy Access ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Radar Networks ◽  
Temporal And Spatial Variability ◽  
Precipitation Estimates ◽  
Precipitation Events

<p>Extreme precipitation events are expected to occur more frequently in a warming climate. Understanding their structure and predicting the exact time and location of precipitation events still remains a challenge because of the high temporal and spatial variability of rainfall. Nationwide weather radar networks are a common tool for investigating precipitation events and their spatial and temporal structure. The German Weather Service (DWD) provides a nationwide climatological radar data set from 2001 to 2020. A reprocessing procedure has been applied to reflectivity measurements in order to obtain precipitation estimates as homogeneous as possible. With an object-oriented analysis, all precipitation events for 11 different durations from 1 to 72 hours exceeding DWD’s official warning level for heavy precipitation have been detected and statistically analysed.</p><p>We will present a comprehensive analysis of all heavy precipitation events that occurred in Germany between 2001 and 2020. We examined their size, duration, location, spatial structure and distribution as well as regional and climatological differences and demonstrate how this information is collected in an online tool for easy access. An assessment of how well these heavy precipitation events were captured by DWD’s network of precipitation stations will be given. Finally, we will present the possibility to use the event detection procedure as an operational tool for assessing and classifying heavy precipitation events and their potential impact in near real-time.</p>

scholarly journals Winter Precipitations of Northern Part of Farming-pastoral Zone and Hulunbuir Grassland

2021 ◽  
Author(s):  
You Xia
Keyword(s):  
Heavy Precipitation ◽  
Northern China ◽  
Total Precipitation ◽  
Wind Fields ◽  
Precipitation Frequency ◽  
The Past ◽  
Monthly Total Precipitation ◽  
Monthly Total ◽  
High Record ◽  
Precipitation Events

Abstract In January 2018 a high record of monthly total precipitation in northern China drew our attention. This number is 4 times more than that in normal winters over the past 30 years, and its location is in northern China. Thus our research region is composed by the northern part of the farming-pastoral zone and the Hulunbuir Grassland. We target our research at understanding the phenomena and causes of such high precipitations. We explore the heavy precipitation locations, and use dynamical analyses on different pressure levels to find out the cause of the high score. We analyze wind fields, geopotential heights and relative humidity for the pressure levels of 200 hPa, 500 hPa, 700 hPa and 850 hPa. We find that the location of the highest monthly total precipitation in January 2018 is on the mountain, whereas the spots of heavy precipitations during one event are not located on the mountain. Zooming in January 2018, it is the precipitation frequency that drastically increased, not the number of heavy precipitation events. The dynamical analyses show that the heavy precipitation events in January 2018 are mainly caused by appearance of cyclones either in or near the research region at high geopotential heights.

Observed Changes in the Distributions of Daily Precipitation Frequency and Amount over China from 1960 to 2013

2015 ◽  
Vol 28 (17) ◽  
pp. 6960-6978 ◽  
Author(s):  
Shuangmei Ma ◽  
Tianjun Zhou ◽  
Aiguo Dai ◽  
Zhenyu Han
Keyword(s):  
Global Warming ◽  
Daily Precipitation ◽  
Heavy Precipitation ◽  
Northern China ◽  
Northwest China ◽  
Precipitation Frequency ◽  
Global Mean Temperature ◽  
Decadal Variations ◽  
Intensity Category ◽  
Precipitation Events

Abstract In this study, daily precipitation (P) records for 1960–2013 from 632 stations in China were homogenized and then applied to study the changes in the frequency of dry (P = 0) and trace (0 < P < 0.1 mm day−1) days and all precipitation events (P ≥ 0.1 mm day−1), and the frequency and accumulated amount of precipitation at different intensities. Over China as a whole, very heavy precipitation (P ≥ 50 mm day) events have increased significantly from 1960 to 2013, while light (0.1 ≤ P < 10 mm day−1) and moderate (10 ≤ P < 25 mm day−1) events have decreased significantly, accompanying the significant increases of dry days and decreases of trace days. This indicates a shift from light to intense precipitation, implying increased risks of drought and floods over China since 1960. Although the consistent increases of dry days and decreases of trace days and light and total precipitation days are seen over most of China, changes in other precipitation categories exhibit clear regional differences. Over the Yangtze River valley and southeast China, very heavy precipitation events have increased while light precipitation events have decreased. However, positive trends are seen for all precipitation categories over northwest China, while trends are generally negative over southwest, northeast, and northern China. To examine the association with global warming, the dependence of the precipitation change for each intensity category over China on global-mean temperature was analyzed using interannual to decadal variations. Results show that dry and trace days and very light and very heavy precipitation events exhibit larger changes per unit global warming than medium-intensity precipitation events.

scholarly journals EMO-5: A high-resolution multi-variable gridded meteorological data set for Europe

2021 ◽  
Author(s):  
Vera Thiemig ◽  
Goncalo N. Gomes ◽  
Jon O. Skøien ◽  
Markus Ziese ◽  
Armin Rauthe-Schöch ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Meteorological Data ◽  
Heavy Precipitation ◽  
Water Vapour Pressure ◽  
Data Set ◽  
High Resolution Data ◽  
Weather Stations ◽  
Resolution Data ◽  
Precipitation Events

Abstract. In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

Real-time bias adjustment for satellite-based precipitation estimates over Mainland China

2021 ◽  
pp. 126133
Author(s):  
Zhehui Shen ◽  
Bin Yong ◽  
Jonathan J. Gourley ◽  
Weiqing Qi
Keyword(s):  
Real Time ◽  
Mainland China ◽  
Bias Adjustment ◽  
Precipitation Estimates

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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