Study of NWP parameterizations on extreme precipitation events over Basque Country

Abstract. The Weather Research and Forecasting model (WRF), like other numerical models, can make use of several parameterization schemes. The purpose of this study is to determine how available cumulus parameterization (CP) and microphysics (MP) schemes in the WRF model simulate extreme precipitation events in the Basque Country. Possible combinations among two CP schemes (Kain–Fritsch and Betts–Miller–Janjic) and five MP (WSM3, Lin, WSM6, new Thompson and WDM6) schemes were tested. A set of simulations, corresponding to 21st century extreme precipitation events that have caused significant flood episodes have been compared with point observational data coming from the Basque Country Automatic Weather Station Mesonetwork. Configurations with Kain–Fritsch CP scheme produce better quantity of precipitation forecast (QPF) than BMJ scheme configurations. Depending on the severity level and the river basin analysed different MP schemes show the best behaviours, demonstrating that there is not a unique configuration that solve exactly all the studied events.

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Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations

Journal of Climate ◽

10.1175/jcli-d-17-0404.1 ◽

2018 ◽

Vol 31 (6) ◽

pp. 2115-2131 ◽

Cited By ~ 13

Author(s):

Steven C. Chan ◽

Elizabeth J. Kendon ◽

Nigel Roberts ◽

Stephen Blenkinsop ◽

Hayley J. Fowler

Keyword(s):

Extreme Precipitation ◽

Land Surface ◽

Large Scale ◽

Future Climate ◽

Cumulus Parameterization ◽

Hourly Precipitation ◽

Vertical Stability ◽

Extreme Precipitation Events ◽

Climate Simulations ◽

Precipitation Events

Midlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present- and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5- and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.

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Developing PIDF Curves From Dynamically Downscaled WRF Model Fields to Examine Extreme Precipitation Events in Three Eastern U.S. Metropolitan Areas

Journal of Geophysical Research Atmospheres ◽

10.1029/2019jd031584 ◽

2019 ◽

Vol 124 (24) ◽

pp. 13895-13913

Author(s):

Anna M. Jalowska ◽

Tanya L. Spero

Keyword(s):

Extreme Precipitation ◽

Metropolitan Areas ◽

Wrf Model ◽

Extreme Precipitation Events ◽

Precipitation Events

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Extreme Precipitation in Models: An Evaluation

Weather and Forecasting ◽

10.1175/waf-d-16-0093.1 ◽

2016 ◽

Vol 31 (6) ◽

pp. 1853-1879 ◽

Cited By ~ 23

Author(s):

Gregory R. Herman ◽

Russ S. Schumacher

Keyword(s):

Extreme Precipitation ◽

Wrf Model ◽

Ensemble Forecast ◽

Forecast Skill ◽

Forecast System ◽

Severe Storms ◽

The North ◽

Extreme Precipitation Events ◽

The U.S ◽

Precipitation Events

Abstract A continental United States (CONUS)-wide framework for analyzing quantitative precipitation forecasts (QPFs) from NWP models from the perspective of precipitation return period (RP) exceedances is introduced using threshold estimates derived from a combination of NOAA Atlas 14 and older sources. Forecasts between 2009 and 2015 from several different NWP models of varying configurations and spatial resolutions are analyzed to assess bias characteristics and forecast skill for predicting RP exceedances. Specifically, NOAA’s Global Ensemble Forecast System Reforecast (GEFS/R) and the National Severe Storms Laboratory WRF (NSSL-WRF) model are evaluated for 24-h precipitation accumulations. The climatology of extreme precipitation events for 6-h accumulations is also explored in three convection-allowing models: 1) NSSL-WRF, 2) the North American Mesoscale 4-km nest (NAM-NEST), and 3) the experimental High Resolution Rapid Refresh (HRRR). The GEFS/R and NSSL-WRF are both found to exhibit similar 24-h accumulation RP exceedance climatologies over the U.S. West Coast to those found in observations and are found to be approximately equally skillful at predicting these exceedance events in this region. In contrast, over the eastern two-thirds of the CONUS, GEFS/R struggles to predict the predominantly convectively driven extreme QPFs, predicting far fewer events than are observed and exhibiting inferior forecast skill to the NSSL-WRF. The NSSL-WRF and HRRR are found to produce 6-h extreme precipitation climatologies that are approximately in accord with those found in the observations, while NAM-NEST produces many more RP exceedances than are observed across all of the CONUS.

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

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-6979-2013 ◽

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.

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Understanding Model-Based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0170.1 ◽

2018 ◽

Vol 19 (2) ◽

pp. 459-475 ◽

Cited By ~ 5

Author(s):

Xiaodong Chen ◽

Faisal Hossain

Keyword(s):

Vertical Velocity ◽

Extreme Precipitation ◽

Numerical Models ◽

Engineering Practice ◽

Probable Maximum Precipitation ◽

Maximum Precipitation ◽

Extreme Precipitation Events ◽

Atmospheric Instability ◽

Moisture Availability ◽

Precipitation Events

Abstract Extreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, the authors present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability, and large-scale convergence over the continental United States (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern U.S. mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domainwide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. These analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm.

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Precipitation Forecast Experiments Using the Weather Research and Forecasting (WRF) Model at Gray-Zone Resolutions

Weather and Forecasting ◽

10.1175/waf-d-18-0026.1 ◽

2018 ◽

Vol 33 (6) ◽

pp. 1605-1616 ◽

Cited By ~ 8

Author(s):

Ji-Young Han ◽

Song-You Hong

Keyword(s):

High Resolution ◽

Model Simulation ◽

Wrf Model ◽

Heavy Precipitation ◽

Grid Size ◽

Cumulus Parameterization ◽

Precipitation Forecast ◽

Weather Research And Forecasting ◽

Gray Zone ◽

Weather Research

Abstract In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage.

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High-Resolution Downscaled Simulations of Warm-Season Extreme Precipitation Events in the Colorado Front Range under Past and Future Climates*

Journal of Climate ◽

10.1175/jcli-d-12-00744.1 ◽

2013 ◽

Vol 26 (21) ◽

pp. 8671-8689 ◽

Cited By ~ 34

Author(s):

Kelly Mahoney ◽

Michael Alexander ◽

James D. Scott ◽

Joseph Barsugli

Keyword(s):

High Resolution ◽

Extreme Events ◽

Extreme Precipitation ◽

Climate Model ◽

Dynamical Downscaling ◽

Wrf Model ◽

Model Errors ◽

Grid Spacing ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors.

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