Forecasting extreme precipitation event over Munsiyari (Uttarakhand) using 3DVAR data assimilation in mesoscale model

2019 ◽  
Vol 129 (1) ◽  
Author(s):  
N Narasimha Rao ◽  
M S Shekhar ◽  
G P Singh
Keyword(s):  
Data Assimilation ◽  
Extreme Precipitation ◽  
Mesoscale Model ◽  
Extreme Precipitation Event ◽  
Precipitation Event

scholarly journals Response of a mixed grass prairie to an extreme precipitation event

Ecosphere ◽  
2015 ◽  
Vol 6 (10) ◽  
pp. art172 ◽  
Author(s):  
Amy L. Concilio ◽  
Janet S. Prevéy ◽  
Peter Omasta ◽  
James O'Connor ◽  
Jesse B. Nippert ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Mixed Grass Prairie ◽  
Mixed Grass

scholarly journals Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

2019 ◽  
Vol 11 (20) ◽  
pp. 2335 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen ◽  
Jiafeng Zheng
Keyword(s):  
Extreme Precipitation ◽  
Rapid Development ◽  
Eastern China ◽  
Rain Gauge ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Distribution Data ◽  
Lake Area ◽  
Polarimetric Radar ◽  
Radar Rainfall

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms.

Extreme precipitation event over the Yellow Sea western coast: Is there a trend?

2017 ◽  
Vol 441 ◽  
pp. 1-17 ◽  
Author(s):  
Huailiang Wang ◽  
Zhuhai Shao ◽  
Tao Gao ◽  
Tao Zou ◽  
Jie Liu ◽  
...  
Keyword(s):  
Yellow Sea ◽  
Extreme Precipitation ◽  
Extreme Precipitation Event ◽  
The Yellow Sea ◽  
Precipitation Event ◽  
Western Coast

scholarly journals Forecasting mesoscale precipitation using the MM5 model with the four-dimensional data assimilation (FDDA) technique

2013 ◽  
Vol 7 (3) ◽  
pp. 258-263
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
System Development ◽  
Time Integration ◽  
Mountain Region ◽  
Precipitation Event ◽  
Model Data ◽  
Interval Model ◽  
Mm5 Model ◽  
Polar Orbiting Satellite

The two basic forms of multi-scale data assimilation procedures (FDDA), based on Newtonian relaxation, of analysis and observations nudging have been applied for precipitation event period occurred over Portugal during summer season, using the Fifth Generation Mesoscale Model (MM5) developed and maintained by the Pennsylvania State University and National Center for Atmospheric Research (PSU/NCAR). The model has been configured for three nested grid domains covering part of the Eastern part of North Atlantic region evolving the Portugal, with 35 vertical levels, from surface up to 100 hPa top level. The model forecasting have been conducted employing daily available data from surface observational network, radio-sounding from Lisbon/Portugal and NOAA-16 polar orbiting satellite retrieved vertical profiles data. The three integration domains of MM5 model have been processed using, as boundary and first guess fields, the global atmospheric forecast NCEP-NWS/AVN model data gathered through the Unidata Local Data Manager (LDM)/Unidata Internet Data Distribution (IDD) system. All daily forecasting, with FDDA and with no FDDA, have been run for 60 hours forecast, with 30 minutes interval model data output to provide enough timely detailed results. The FDDA analysis presented a quite reasonable data ingesting volume of almost all available satellite data, with the exception of humidity data retrieved for high levels, above around 500 hPa. The obtained results indicate that, even using weak FDDA constraint coefficient values, presents a significant improvement in the numerical prognosis in the precipitation field, on both space and time integration levels. The results also presented an enhancement of the physics of the convective mesoscale system development, particularly over mountain region, indicating that it would be interesting to conduct an experiment with a dense data collecting platform coverage focused on events which occur in some prevailing mountain region of Portugal.

scholarly journals On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe

2021 ◽  
Author(s):  
Alexandre Tuel ◽  
Bettina Schaefli ◽  
Jakob Zscheischler ◽  
Olivia Martius
Keyword(s):  
Extreme Precipitation ◽  
Temporal Structure ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Discharge Data ◽  
High Discharge ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
The Impact ◽  
Precipitation Events

Abstract. River discharge is impacted by the sub-seasonal (weekly to monthly) temporal structure of precipitation. One example is the successive occurrence of extreme precipitation events over sub-seasonal timescales, referred to as temporal clustering. Its potential effects on discharge have received little attention. Here, we address this question by analysing discharge observations following extreme precipitation events either clustered in time or occurring in isolation. We rely on two sets of precipitation and discharge data, one centered on Switzerland and the other over Europe. We identify "clustered" extreme precipitation events based on the previous occurrence of another extreme precipitation within a given time window. We find that clustered events are generally followed by a more prolonged discharge response with a larger amplitude. The probability of exceeding the 95th discharge percentile in the five days following an extreme precipitation event is in particular up to twice as high for situations where another extreme precipitation event occurred in the preceding week compared to isolated extreme precipitation events. The influence of temporal clustering decreases as the clustering window increases; beyond 6–8 weeks the difference with non-clustered events is negligible. Catchment area, streamflow regime and precipitation magnitude also modulate the response. The impact of clustering is generally smaller in snow-dominated and large catchments. Additionally, particularly persistent periods of high discharge tend to occur in conjunction with temporal clusters of precipitation extremes.

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