Observations and Predictability of a High-Impact Narrow Cold-Frontal Rainband over Southern California on 2 February 2019

2020 ◽  
Vol 35 (5) ◽  
pp. 2083-2097 ◽  
Author(s):  
Forest Cannon ◽  
Nina S. Oakley ◽  
Chad W. Hecht ◽  
Allison Michaelis ◽  
Jason M. Cordeira ◽  
...  
Keyword(s):  
Southern California ◽  
Weather Prediction ◽  
Wrf Model ◽  
High Impact ◽  
Physical Processes ◽  
Research Attention ◽  
Spatial And Temporal Scales ◽  
Atmospheric River ◽  
Forecast Lead Time ◽  
Forecast Guidance

AbstractShort-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, specific contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance field campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24 h later, are used to demonstrate that a configuration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numerical weather prediction and instill confidence in the utility of WRF as a forecast guidance tool for short- to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

2015 ◽  
Vol 54 (8) ◽  
pp. 1809-1825 ◽  
Author(s):  
Yaodeng Chen ◽  
Hongli Wang ◽  
Jinzhong Min ◽  
Xiang-Yu Huang ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Liquid Water ◽  
Weather Prediction ◽  
Wrf Model ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water ◽  
The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

scholarly journals Evaluation of the Moisture Sources in two Extreme Landfalling Atmospheric River Events using an Eulerian WRF-Tracers tool

2017 ◽  
Author(s):  
Jorge Eiras-Barca ◽  
Francina Dominguez ◽  
Huancui Hu ◽  
A. Daniel Garaboa-Paz ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Cross Sections ◽  
North Atlantic ◽  
Cold Front ◽  
Wrf Model ◽  
Atmospheric River ◽  
The North ◽  
Northern Latitudes ◽  
The Pacific ◽  
Low Level Jet ◽  
Tropical Moisture

Abstract. A new 3D Tracer tool is coupled to the WRF model to analyze the origin of the moisture in two extreme Atmospheric River (AR) events: the so-called Great Coast Gale of 2007 in the Pacific Basin, and the Great Storm of 1987 in the North Atlantic. Results show that between 80 % and 90 % of the moisture advected by the ARs, as well as between 70 % and 80 % of the associated precipitation have a tropical or subtropical origin. Local convergence transport is responsible for the remaining moisture and precipitation. The ratio of tropical moisture to total moisture is maximized as the cold front arrives to land. Vertical cross sections of the moisture suggest that the maximum in humidity does not necessarily coincide with the Low-Level Jet (LLJ) of the extratropical cyclone. Instead, the amount of tropical humidity is maximized in the lowest atmospheric level in southern latitudes, and can be located above, below or ahead the LLJ in northern latitudes in both analyzed cases.

scholarly journals Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 378 ◽  
Author(s):  
Channa Rodrigo ◽  
Sangil Kim ◽  
Il Jung
Keyword(s):  
Sri Lanka ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Wrf Model ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Sensitivity Study ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Pressure System

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.

scholarly journals Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Tien Du Duc ◽  
Lars Robert Hole ◽  
Duc Tran Anh ◽  
Cuong Hoang Duc ◽  
Thuy Nguyen Ba
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Regional Models ◽  
Global Spectral Model ◽  
Numerical Weather ◽  
The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

scholarly journals Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

2016 ◽  
Vol 31 (3) ◽  
pp. 1001-1017 ◽  
Author(s):  
Omar V. Müller ◽  
Miguel A. Lovino ◽  
Ernesto H. Berbery
Keyword(s):  
South America ◽  
Lead Time ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Southern South America ◽  
Climate Conditions ◽  
Forecast Lead Time ◽  
Highly Correlated ◽  
Precipitation Season

Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

scholarly journals The Impact of Initial Snow Conditions on the Numerical Weather Simulation of a Northern Rockies Atmospheric River

2021 ◽  
Vol 22 (1) ◽  
pp. 155-167
Author(s):  
William Rudisill ◽  
Alejandro Flores ◽  
James McNamara
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Radiative Properties ◽  
Numerical Weather ◽  
Atmospheric River ◽  
Surface Snow ◽  
Snow Conditions ◽  
The Impact

AbstractSnow’s thermal and radiative properties strongly impact the land surface energy balance and thus the atmosphere above it. Land surface snow information is poorly known in mountainous regions. Few studies have examined the impact of initial land surface snow conditions in high-resolution, convection-permitting numerical weather prediction models during the midlatitude cool season. The extent to which land surface snow influences atmospheric energy transport and subsequent surface meteorological states is tested using a high-resolution (1 km) configuration of the Weather Research and Forecasting (WRF) Model, for both calm conditions and weather characteristic of a warm late March atmospheric river. A set of synthetic but realistic snow states are used as initial conditions for the model runs and the resulting differences are compared. We find that the presence (absence) of snow decreases (increases) 2-m air temperatures by as much as 4 K during both periods, and that the atmosphere responds to snow perturbations through advection of moist static energy from neighboring regions. Snow mass and snow-covered area are both important variables that influence 2-m air temperature. Finally, the meteorological states produced from the WRF experiments are used to force an offline hydrologic model, demonstrating that snowmelt rates can increase/decrease by factor of 2 depending on the initial snow conditions used in the parent weather model. We propose that more realistic representations of land surface snow properties in mesoscale models may be a source of hydrometeorological predictability

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

2020 ◽  
Author(s):  
Qiang Dai ◽  
Jingxuan Zhu ◽  
Shuliang Zhang ◽  
Shaonan Zhu ◽  
Dawei Han ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Large Scale ◽  
Weather Prediction ◽  
Wrf Model ◽  
Analysis Data ◽  
Potential Effect ◽  
Rainfall Erosivity ◽  
Size Distributions ◽  
Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driving force for soil erosion and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy (KE). As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops KE. With the development of global atmospheric re-analysis data, numerical weather prediction (NWP) techniques become a promising way to estimate rainfall KE directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware [TAA]) to obtain raindrop size distributions, rainfall KE and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, TAA had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

scholarly journals Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

2020 ◽  
Vol 24 (11) ◽  
pp. 5407-5422
Author(s):  
Qiang Dai ◽  
Jingxuan Zhu ◽  
Shuliang Zhang ◽  
Shaonan Zhu ◽  
Dawei Han ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Water Conservation ◽  
Large Scale ◽  
Weather Prediction ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Erosivity ◽  
Size Distributions ◽  
Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

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