scholarly journals Sensitivity of the WRF model to the lower boundary in an extreme precipitation event – Madeira Island case study

2013 ◽  
Vol 1 (5) ◽  
pp. 5603-5641 ◽  
Author(s):  
J. C. Teixeira ◽  
A. C. Carvalho ◽  
T. Luna ◽  
A. Rocha

Abstract. Through the years, the advances in satellite technology made feasible the acquisition of information about the Earth surface, such as elevation and land use with great detail and resolution. This information can be included in numerical atmospheric models, updating them and providing a more detailed lower boundary, which in turn can improve the results of events forced by it. Given this, this work aims to study the sensitivity of the Weather Research and Forecast model to three different topography datasets as well as two different land use datasets in an extreme precipitation event. A test case study in which topography driven precipitation was dominant over Madeira Island was considered which triggered several flash floods and mudslides in the southern parts of the island. Model results show higher model skill in precipitation over Madeira leeward and in the windward wind flow, in spite of the non significant enhancement on the overall results with higher resolution datasets of topography and land use.

2014 ◽  
Vol 14 (8) ◽  
pp. 2009-2025 ◽  
Author(s):  
J. C. Teixeira ◽  
A. C. Carvalho ◽  
M. J. Carvalho ◽  
T. Luna ◽  
A. Rocha

Abstract. The advances in satellite technology in recent years have made feasible the acquisition of high-resolution information on the Earth's surface. Examples of such information include elevation and land use, which have become more detailed. Including this information in numerical atmospheric models can improve their results in simulating lower boundary forced events, by providing detailed information on their characteristics. Consequently, this work aims to study the sensitivity of the weather research and forecast (WRF) model to different topography as well as land-use simulations in an extreme precipitation event. The test case focused on a topographically driven precipitation event over the island of Madeira, which triggered flash floods and mudslides in the southern parts of the island. Difference fields between simulations were computed, showing that the change in the data sets produced statistically significant changes to the flow, the planetary boundary layer structure and precipitation patterns. Moreover, model results show an improvement in model skill in the windward region for precipitation and in the leeward region for wind, in spite of the non-significant enhancement in the overall results with higher-resolution data sets of topography and land use.


2011 ◽  
Vol 11 (9) ◽  
pp. 2437-2452 ◽  
Author(s):  
T. Luna ◽  
A. Rocha ◽  
A. C. Carvalho ◽  
J. A. Ferreira ◽  
J. Sousa

Abstract. In the morning of the 20 February of 2010 an extreme precipitation event occurred over Madeira Island. This event triggered several flash floods and mudslides in the southern parts of the island, resulting in 42 confirmed deaths, 100 injured, and at least 8 people still missing. These extreme weather conditions were associated to a weather frontal system moving northeastwards embedded in a low pressure area centered in the Azores archipelago. This storm was one in a series of such storms that affected Portugal, Spain, Morocco and the Canary islands causing flooding and strong winds. These storms were bolstered by an unusually strong sea surface temperature gradient across the Atlantic Ocean. In this study, the WRF model is used to evaluate the intensity and predictability of this precipitation extreme event over the island. The synoptic/orographic nature of the precipitation is also evaluated, as well as the sensitivity of the model to horizontal resolution and cumulus parameterization. Orography was found to be the main factor explaining the occurrence, amplitude and phase of precipitation over the Island.


2010 ◽  
Vol 29 (3) ◽  
pp. 330-344 ◽  
Author(s):  
Elisabeth Schlosser ◽  
Jordan G. Powers ◽  
Michael G. Duda ◽  
Kevin W. Manning ◽  
Carleen H. Reijmer ◽  
...  

2020 ◽  
Vol 100 (2) ◽  
pp. 635-654 ◽  
Author(s):  
Shailendra Pratap ◽  
Prashant K. Srivastava ◽  
Ashish Routray ◽  
Tanvir Islam ◽  
Rajesh Kumar Mall

2016 ◽  
Author(s):  
Elcin Tan

Abstract. Providing high accuracy in quantitative extreme precipitation forecasting (QEPF) is still a challenge. California is vulnerable to extreme precipitation, which occurs due to atmospheric rivers and might be more intense with climate change. Accordingly, this study is an attempt to evaluate the extreme precipitation forecasting performance of a QPF model, the Weather Research and Forecast Model, version 3.1.7, for the extreme precipitation event that caused the 1997 New Year’s flood in California. Sensitivities of 19 microphysics schemes are tested by utilizing 18 various Goodness of Fit (GoF) tests for hourly and point-wise comparisons between 3-km horizontal domain resolution simulations of the WRF Model and observations. The results indicate that the coefficient of persistence (cp) is the first metric that needs to be evaluated because it determines whether simulation versus observation values are reasonable. Comparisons of 3 out of 8 stations in the American River Watershed passed this test. The results also show that Normalized Root Mean Square Errors (NRMSE) and Percent Bias (PBIAS) metrics are more representative than others due to their ability to discriminate model performances. Further, microphysics (MP) schemes are also significantly sensitive to location. Although 3 of the stations that passed the cp test are quite near to each other spatially, different MP schemes become prominent for different observation locations. For instance, for the ALP station, MP3, MP8, MP17, and MP28 indicate better performances, whereas the errors of MP3, MP8, MP9, and MP17 are less than other MPs for the BTA station. However, MP11 has the only reasonable results, according to cp values for the CAP station. The MPs are also evaluated for 72-hr and basin-averaged precipitation estimations of the WRF Model by means of true percent relative errors. The results show that the accuracy of the WRF Model is much higher for the 72-hr total basin-averaged evaluations than for the hourly and point-wise comparisons. Thus, the Thompson Scheme (MP8) indicates more trustworthy results than others, with a 3.1 % true percent relative error. Although WRF simulations overestimate the 72-hr basin-averaged precipitation for most of the MP schemes, this may not be pronounced for moderate, heavy, and extreme precipitation when hourly and point-wise evaluations are performed but is valid for light precipitation.


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

2019 ◽  
Vol 11 (20) ◽  
pp. 2335 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen ◽  
Jiafeng Zheng

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.


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