Impact of increasing horizontal and vertical resolution during the HWRF hybrid EnVar data assimilation on the analysis and prediction of Hurricane Patricia (2015)

2020 ◽  
Author(s):  
Jie Feng ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Horizontal Resolution ◽  
Tropical Storm ◽  
Weather Research And Forecasting ◽  
Vertical Resolution ◽  
Model Resolution ◽  
Warm Core ◽  
The Individual ◽  
The Impact ◽  
Hwrf Model

AbstractAlthough numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for Hurricane Weather Research and Forecasting (HWRF) model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia.The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary, secondary circulation, and warm core structures via the increased resolution in DA lead to improved TC intensity forecasts.

Impact of Increasing Horizontal and Vertical Resolution during the HWRF Hybrid EnVar Data Assimilation on the Analysis and Prediction of Hurricane Patricia (2015)

2021 ◽  
Author(s):  
Jie Feng
Keyword(s):  
Data Assimilation ◽  
Horizontal Resolution ◽  
Tropical Storm ◽  
Weather Research And Forecasting ◽  
Vertical Resolution ◽  
Model Resolution ◽  
Warm Core ◽  
The Individual ◽  
The Impact ◽  
Hwrf Model

<p>Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking.  In this study, using the ensemble-variational DA system for Hurricane Weather Research and Forecasting (HWRF) model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015).  The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia.</p><p>The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage.  These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage.  Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages.  Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary, secondary circulation, and warm core structures via the increased resolution in DA lead to improved TC intensity forecasts.</p>

scholarly journals Moisture Recycling over the Iberian Peninsula: The Impact of 3DVAR Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 19
Author(s):  
Santos J. González-Rojí ◽  
Jon Sáenz ◽  
Javier Díaz de Argandoña ◽  
Gabriel Ibarra-Berastegi
Keyword(s):  
Data Assimilation ◽  
Iberian Peninsula ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Spatiotemporal Distribution ◽  
Weather Research And Forecasting ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
The Impact ◽  
Weather Research

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.

scholarly journals Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

2021 ◽  
Vol 13 (11) ◽  
pp. 2103
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang
Keyword(s):  
Data Assimilation ◽  
Northern China ◽  
Horizontal Resolution ◽  
Radar Reflectivity ◽  
Small Scale ◽  
Time Interval ◽  
Outer Domain ◽  
Storm Prediction ◽  
The Impact ◽  
Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

On the role of vertical and horizontal model resolution for the simulation of stratospheric transport

2021 ◽  
Author(s):  
Hella Garny ◽  
Simone Dietmüller ◽  
Roland Eichinger ◽  
Aman Gupta ◽  
Marianna Linz
Keyword(s):  
Climate Models ◽  
Horizontal Resolution ◽  
Climate Forcing ◽  
Numerical Dispersion ◽  
Mixing Efficiency ◽  
Vertical Resolution ◽  
Model Resolution ◽  
Residual Circulation ◽  
Year 2000

<p>The stratospheric transport circulation, or Brewer-Dobson Circulation (BDC), is often conceptually seperated into advection along the residual circulation and two-way mixing. In particular the latter part has recently been found to exert a strong influence on inter-model differences of mean age of Air (AoA), a common measure of the BDC. However, the precise reason for model differences in two-way mixing remains unknown, as many model<br>components in multi-model projects differ. One component that likely plays an important role is model resolution, both vertically and horizontally. To analyse this aspect, we carried out a set of simulations with identical and constant year 2000 climate forcing varying the spectral horizontal<br>resolution (T31,T42,T63,T85) and the number of vertical levels (L31,L47,L90). We find that increasing the vertical resolution leads to an increase in mean AoA. Most of this change can be attributed to aging by mixing. The mixing efficiency, defined as the ratio of isentropic mixing strength and the diabatic circulation, shows the same dependency on vertical resolution. While horizontal resolution changes do not systematically change mean AoA, we do<br>find a systematic decrease in the mixing efficiency with increasing horizontal resolution. Non-systematic changes in the residual circulation partly compensate the mixing efficiency changes, leading to the non-systematic mean AoA changes. The mixing efficiency changes with vertical and horizontal resolution are consistent with expectations on the effects of numerical dispersion on mean AoA. To further investigate the most relevant regions of mixing differences, we analyse height-resolved mixing efficiency differences. Overall, this work will help to shed light on the underlying reasons for the large biases of climate models in simulating stratospheric transport.</p>

scholarly journals A Case Study of Observed and Modeled Barrier Flow in the Denmark Strait in May 2015

2017 ◽  
Vol 145 (6) ◽  
pp. 2385-2404 ◽  
Author(s):  
Alice K. DuVivier ◽  
John J. Cassano ◽  
Steven Greco ◽  
G. David Emmitt
Keyword(s):  
Wrf Model ◽  
Horizontal Resolution ◽  
Surface Energy Budget ◽  
Vertical Resolution ◽  
Structure Comparison ◽  
Denmark Strait ◽  
Pbl Scheme ◽  
Barrier Jets ◽  
The Impact ◽  
Jet Structure

Abstract Mesoscale barrier jets in the Denmark Strait are common in winter months and have the capability to influence open ocean convection. This paper presents the first detailed observational study of a summertime (21 May 2015) barrier wind event in the Denmark Strait using dropsondes and observations from an airborne Doppler wind lidar (DWL). The DWL profiles agree well with dropsonde observations and show a vertically narrow (~250–400 m) barrier jet of 23–28 m s−1 near the Greenland coast that broadens (~300–1000 m) and strengthens farther off coast. In addition, otherwise identical regional high-resolution Weather Research and Forecasting (WRF) Model simulations of the event are analyzed at four horizontal grid spacings (5, 10, 25, and 50 km), two vertical resolutions (40 and 60 levels), and two planetary boundary layer (PBL) parameterizations [Mellor–Yamada–Nakanishi–Niino, version 2.5 (MYNN2.5) and University of Washington (UW)] to determine what model configurations best simulate the observed jet structure. Comparison of the WRF simulations with wind observations from satellites, dropsondes, and the airborne DWL scans indicate that the combination of both high horizontal resolution (5 km) and vertical resolution (60 levels) best captures observed barrier jet structure and speeds as well as the observed cloud field, including some convective clouds. Both WRF PBL schemes produced reasonable barrier jets with the UW scheme slightly outperforming the MYNN2.5 scheme. However, further investigation at high horizontal and vertical resolution is needed to determine the impact of the WRF PBL scheme on surface energy budget terms, particularly in the high-latitude maritime environment around Greenland.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

Fog forecasting for Schiphol airport at sub-kilometre scale.  

2021 ◽  
Author(s):  
Gert-Jan Steeneveld ◽  
Roosmarijn Knol
Keyword(s):  
Spatial Resolution ◽  
Numerical Models ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Physical Processes ◽  
Radiation Fog ◽  
Fog Forecasting ◽  
Elevation Data ◽  
The Individual ◽  
The Impact

<p>Fog is a critical weather phenomenon for safety and operations in aviation. Unfortunately, the forecasting of radiation fog remains challenging due to the numerous physical processes that play a role and their complex interactions, in addition to the vertical and horizontal resolution of the numerical models. In this study we evaluate the performance of the Weather Research and Forecasting (WRF) model for a radiation fog event at Schiphol Amsterdam Airport (The Netherlands) and further develop the model towards a 100 m grid spacing. Hence we introduce high resolution land use and land elevation data. In addition we study the role of gravitational droplet settling, advection of TKE, top-down diffusion caused by strong radiative cooling at the fog top. Finally the impact of heat released by the terminal areas on the fog formation is studied. The model outcomes are evaluated against 1-min weather observations near multiple runways at the airport.</p><p>Overall we find the WRF model shows an reasonable timing of the fog onset and is well able to reproduce the visibility and meteorological conditions as observed during the case study. The model appears to be relatively insensitive to the activation of the individual physical processes. An increased spatial resolution to 100 m generally results in a better timing of the fog onset differences up to three hours, though not for all runways. The effect of the refined landuse dominates over the effect of refined elevation data. The modelled fog dissipation systematically occurs 3-4 h hours too early, regardless of physical processes or spatial resolution. Finally, the introduction of heat from terminal buildings delays the fog onset with a maximum of two hours, an overestimated visibility of 100-200 m and a decrease of the LWC with 0.10-0.15 g/kg compared to the reference.</p>

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals Improving Hurricane Analyses and Predictions with TCI, IFEX Field Campaign Observations, and CIMSS AMVs Using the Advanced Hybrid Data Assimilation System for HWRF. Part I: What is Missing to Capture the Rapid Intensification of Hurricane Patricia (2015) when HWRF is already Initialized with a More Realistic Analysis?

2019 ◽  
Vol 147 (4) ◽  
pp. 1351-1373 ◽  
Author(s):  
Xu Lu ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Surface Wind ◽  
Rapid Intensification ◽  
Model Resolution ◽  
Field Campaign ◽  
Horizontal Diffusion ◽  
Intensity Forecast ◽  
Physics Parameterizations ◽  
Hwrf Model

Abstract Assimilating inner-core observations collected from recent field campaign programs such as Tropical Cyclone Intensity (TCI) and Intensity Forecasting Experiment (IFEX) together with the enhanced atmospheric motion vectors (AMVs) produce realistic three-dimensional (3D) analyses using the newly developed GSI-based, continuously cycled, dual-resolution hybrid ensemble–variational data assimilation (DA) system for the Hurricane Weather Research and Forecasting (HWRF) Model for Hurricane Patricia (2015). However, more persistent surface wind maximum spindown is found in the intensity forecast initialized from the realistic analyses produced by the DA system but not from the unrealistic initial conditions produced through vortex modification. Diagnostics in this study reveal that the spindown issue is likely attributed to the deficient HWRF Model physics that are unable to maintain the realistic 3D structures from the DA analysis. The horizontal diffusion is too strong to maintain the realistically observed vertical oscillation of radial wind near the eyewall region. The vertical diffusion profile cannot produce a sufficiently strong secondary circulation connecting the realistically elevated upper-level outflow produced in the DA analysis. Further investigations with different model physics parameterizations demonstrate that spindown can be alleviated by modifying model physics parameterizations. In particular, a modified turbulent mixing parameterization scheme together with a reduced horizontal diffusion is found to significantly alleviate the spindown issue and to improve the intensity forecast. Additional experiments show that the peak-simulated intensity and rapid intensification rate can be further improved by increasing the model resolution. But the model resolution is not as important as model physics in the spindown alleviation.

scholarly journals The Impact of Airborne Doppler Aerosol Wind (DAWN) Lidar Wind Profiles on Numerical Simulations of Tropical Convective Systems during the NASA Convective Processes Experiment (CPEX)

2020 ◽  
Vol 37 (4) ◽  
pp. 705-722 ◽  
Author(s):  
Zhiqiang Cui ◽  
Zhaoxia Pu ◽  
G. David Emmitt ◽  
Steven Greco
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Tropical Storm ◽  
Convective System ◽  
Spatiotemporal Resolution ◽  
Convective Systems ◽  
Convective Processes ◽  
Wind Profiles ◽  
The Impact

AbstractHigh-spatiotemporal-resolution airborne Doppler Aerosol Wind (DAWN) lidar profiles over the Caribbean Sea and Gulf of Mexico region were collected during the NASA Convective Processes Experiment (CPEX) field campaign from 27 May to 24 June 2017. This study examines the impact of assimilating these wind profiles on the numerical simulation of moist convective systems using an Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW). A mesoscale convective system and a tropical storm (Cindy) that occurred on 16 June 2017 in a strong shear environment and on 21 June 2017 in a weak shear environment, respectively, are selected as case studies. The DAWN wind profiles are assimilated with the NCEP Gridpoint Statistical Interpolation analysis system using a three-dimensional variational (3DVar) and a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation systems to provide the initial conditions for a short-range forecast. Results show that the assimilation of DAWN wind profiles has significant positive impacts on convective simulations with the two assimilation approaches. The assimilation of DAWN wind profiles creates notable adjustments in the analysis of the divergence field for WRF simulations with a good agreement of wind forecasts with radiosonde observations. The quantitative precipitation forecasting is also improved. In general, the 3DEnVar data assimilation method is deemed more promising for DAWN data assimilation. There are cases with Tropical Storm Cindy in which DAWN data have slight to neutral impact on rainfall forecasts with 3DVAR, implying complicated interactions between errors of retrieved wind data and background error covariance in the lower and upper troposphere.

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