scholarly journals Model Sensitivity Evaluation for 3DVAR Data Assimilation Applied on WRF with a Nested Domain Configuration

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 682
Author(s):  
Mingchun Lam ◽  
Jimmy Chihung Fung
Keyword(s):  
State Of The Art ◽  
Wrf Model ◽  
Summer Season ◽  
Weather Research And Forecasting ◽  
Initial Condition ◽  
Sea Level Pressure ◽  
Sensitivity Evaluation ◽  
Domain Configuration ◽  
Different Seasons ◽  
Atmospheric State

An initial condition that closely represents the true atmospheric state can minimize errors that propagate into the future, and could theoretically lead to improvements in the forecast. This study aims to evaluate and understand the impacts of 3DVAR on the state-of-the-art Weather Research and Forecasting (WRF) model with a two nested domains setup. The domain configuration of the model covers China with an emphasis on Guangdong province, with a resolution of 27 km, 9 km, and 3 km. Improvements in the forecasts for the Winter and Summer season of all the domains are systematically compared and are quantified in terms of 2 m temperature, 10 m wind speed, sea level pressure, and 2 m relative humidity. The results show that 3DVAR provides significant improvements in the winter case and surprisingly improvements were also found after the 48 h of the forecast. Evaluations of performance of 3DVAR in different domains and between two different seasons were done to further understand the reasons behind the discrepancies.

scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

scholarly journals Impact of Coupling an Ocean Model to WRF Nor’easter Simulations

2015 ◽  
Vol 143 (12) ◽  
pp. 4997-5016 ◽  
Author(s):  
Stephen D. Nicholls ◽  
Steven G. Decker
Keyword(s):  
Storm Track ◽  
Wrf Model ◽  
Ocean Model ◽  
Weather Research And Forecasting ◽  
Model Coupling ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Simulated Precipitation ◽  
Ocean Atmosphere ◽  
The Impact

Abstract The impact of ocean–atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor’easter did tend to cool more strongly during the early season (October–December) and were more likely to warm late in the season (February–April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31–0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean–atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%–2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals Two Overlooked Biases of the Advanced Research WRF (ARW) Model in Geopotential Height and Temperature

2012 ◽  
Vol 140 (12) ◽  
pp. 3907-3918 ◽  
Author(s):  
Tae-Kwon Wee ◽  
Ying-Hwa Kuo ◽  
Dong-Kyou Lee ◽  
Zhiquan Liu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Potential Temperature ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Initial Condition ◽  
Forecast Bias ◽  
Hydrostatic Equation ◽  
Arw Model ◽  
Occultation Data ◽  
Bias Corrections

Abstract The authors have discovered two sizeable biases in the Weather Research and Forecasting (WRF) model: a negative bias in geopotential and a warm bias in temperature, appearing both in the initial condition and the forecast. The biases increase with height and thus manifest themselves at the upper part of the model domain. Both biases stem from a common root, which is that vertical structures of specific volume and potential temperature are convex functions. The geopotential bias is caused by the particular discrete hydrostatic equation used in WRF and is proportional to the square of the thickness of model layers. For the vertical levels used in this study, the bias far exceeds the gross 1-day forecast bias combining all other sources. The bias is fixed by revising the discrete hydrostatic equation. WRF interpolates potential temperature from the grids of an external dataset to the WRF grids in generating the initial condition. Associated with the Exner function, this leads to the marked bias in temperature. By interpolating temperature to the WRF grids and then computing potential temperature, the bias is removed. The bias corrections developed in this study are expected to reduce the disparity between the forecast and observations, and eventually to improve the quality of analysis and forecast in the subsequent data assimilation. The bias corrections might be especially beneficial to assimilating height-based observations (e.g., radio occultation data).

scholarly journals Horizontal divergence of typhoon-generated gravity waves in the upper troposphere and lower stratosphere (UTLS) and its influence on typhoon evolution

2014 ◽  
Vol 14 (7) ◽  
pp. 3175-3182 ◽  
Author(s):  
S. H. Kim ◽  
H.-Y. Chun ◽  
W. Jang
Keyword(s):  
Gravity Waves ◽  
Lower Stratosphere ◽  
Inner Core ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Upper Troposphere ◽  
Convective Clouds ◽  
Power Spectral ◽  
Spiral Rainbands

Abstract. The characteristics of horizontal divergence induced by typhoon-generated gravity waves (HDTGWs) and the influence of HDTGW on typhoon evolution are investigated based on the simulation results of Typhoon Saomai (2006) using the Weather Research and Forecasting (WRF) model. The power spectral density of HDTGW shows dominant powers at horizontal wavelengths of 20–30 km and at periods of less than 1 h. This is associated with gravity waves generated by vigorous convective clouds in an inner core region of the typhoon. However, the domain-averaged HDTGW in the upper troposphere and lower stratosphere had a spectral peak at 24 h, which is well correlated with the minimum sea-level pressure of the typhoon, especially during a rapidly developing period. The 24 h period of the averaged HDTGW stems from the inertia–gravity waves generated by the convective clouds in the spiral rainbands, and showed no clear association with the thermal tides or the diurnal variation of precipitation.

scholarly journals Does Increasing Model Resolution Improve the Real-Time Forecasts of Western North Pacific Tropical Cyclones?

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 776
Author(s):  
Jihong Moon ◽  
Jinyoung Park ◽  
Dong-Hyun Cha
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Lower Latitude ◽  
Weather Research And Forecasting ◽  
Track Forecast ◽  
Forecast Errors ◽  
Test Cases ◽  
Model Resolution ◽  
Sea Level Pressure

In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The experiment, with a coarse resolution of 12 km, could not significantly capture the intensification process, especially for maximum intensities (>60 m s−1). The intense TCs were better predicted by experiments using a moving nesting domain with a horizontal resolution of 4 km. The forecast errors for maximum wind speed and minimum sea-level pressure decreased in the experiment with higher resolution; the forecast of lifetime maximum intensity was improved. For the track forecast, the experiment with a coarser resolution tended to simulate TC tracks deviating rightward to the TC motions in the best-track data; this erroneous deflection was reduced in the experiment with a higher resolution. In particular, the track forecast in the experiment with a higher resolution improved more frequently for intense TCs that were generally distributed at relatively lower latitudes among the test cases. The sensitivity of the track forecast to the model resolution was relatively significant for lower-latitude TCs. On the other hand, the track forecasts of TCs moving to the mid-latitudes, which were primarily influenced by large-scale features, were not sensitive to the resolution.

scholarly journals The Contribution of Lake Enhancement to Extreme Snowfall within the Chicago–Milwaukee Urban Corridor during the 2011 Groundhog Day Blizzard

2017 ◽  
Vol 145 (6) ◽  
pp. 2405-2420 ◽  
Author(s):  
Nathan D. Owens ◽  
Robert M. Rauber ◽  
Brian F. Jewett ◽  
Greg M. McFarquhar
Keyword(s):  
Surface Temperature ◽  
Wrf Model ◽  
Lower Atmosphere ◽  
Weather Research And Forecasting ◽  
Structure Stability ◽  
Sea Level Pressure ◽  
Dewpoint Temperature ◽  
The Impact ◽  
Urban Corridor ◽  
The City

Abstract This paper examines the impact of the Laurentian Great Lakes (GL) on atmospheric structure, stability, and precipitation within the Chicago–Milwaukee urban corridor during the passage of the 1–2 February 2011 extratropical cyclone. This storm produced the third largest snowfall [53.8 cm (21.2 in.)] recorded in a 130-yr period in the city of Chicago. Two simulations of the storm using the Weather Research and Forecasting (WRF) Model are described: the first with the GL present, and the second with the lakes replaced with land having characteristics of adjacent shores. The GL were found to alter the surface temperature and moisture fields in their lee during cyclone passage. The changes were limited to the layer below the frontal inversion, but were significant enough to reduce the mean sea level pressure in some locations by 2.0–2.5 hPa, and raise the surface temperature and dewpoint temperature by 2°–4°C across several states downwind. In the Chicago–Milwaukee metropolitan corridor where the heavy snow occurred, the surface temperature and dewpoint temperature increased from +3° to +6°C as a result of heating and moistening of the lower atmosphere by the GL. Enhanced convergence also occurred along the downwind shoreline. Despite these changes, the areal impact on precipitation was surprisingly small, with liquid equivalent precipitation increases exceeding 5 mm limited to a small area over metropolitan Chicago late in the storm. The reason for the limited impact appeared to be the shallow nature of the cold air mass below the frontal inversion. Nevertheless, over metropolitan Chicago, as much as 20% of the snowfall could be attributed to the presence of the GL.

scholarly journals Impacts of Topsoil and Surface Water Salinity on Agriculture: A Study at Paikgachha, Khulna

2021 ◽  
Vol 8 (2) ◽  
pp. 25-32
Author(s):  
Md Ferdous ur Rahman Bhuiya ◽  
Md Humayun Kabir ◽  
Muhammad Ferdaus
Keyword(s):  
Tropical Cyclone ◽  
West Bengal ◽  
Small Deviation ◽  
Wrf Model ◽  
India Meteorological Department ◽  
Horizontal Resolution ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Surface Water Salinity ◽  
Maximum Sustained Wind Speed

Studying the structure, intensity and track of tropical cyclone is very important in effective tropical cyclone warning. In this study, an attempt has been made to simulate the Super Cyclone Amphan to reproduce the structure, intensity and track of the storm that occurred over the Bay of Bengal and made landfall over the coastal zone of Sundarban between Western Bangladesh and Eastern West Bengal of India on 20 May 2020. The Weather Research and Forecasting (WRF) Model was run 120 hours from 0000 UTC of 16 May to 0000 UTC of 21 May 2021 with 9 km horizontal resolution to simulate the selected storm. The model simulated intensity and track of the storm were compared with that of best track data of India Meteorological Department (IMD). The results obtained from the WRF model indicated that the intensity of the selected cyclone in terms of Mean Sea Level Pressure (MSLP) and Maximum Sustained Wind speed (MSW) were 905 hPa and 243 kph whereas the observed MSLP and MSW were close to 920 hPa and 241 kph respectively. It was also indicated that the model predicted the track of the cyclone reasonably well and it was quite close to the best track data throughout its path till landfall with very small deviation and the cyclone made landfall at 7-8 hours before the actual landfall with 167.4 km position error. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(2), 2019, P 25-32

scholarly journals Horizontal divergence of typhoon-generated gravity waves in the upper troposphere and lower stratosphere (UTLS) and its influence on typhoon evolution

2013 ◽  
Vol 13 (11) ◽  
pp. 28953-28972
Author(s):  
S. H. Kim ◽  
H.-Y. Chun ◽  
W. Jang
Keyword(s):  
Gravity Waves ◽  
Lower Stratosphere ◽  
Inner Core ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Upper Troposphere ◽  
Convective Clouds ◽  
Power Spectral ◽  
Spiral Rainbands

Abstract. The characteristics of horizontal divergence induced by typhoon-generated gravity waves (HDTGW) and the influence of HDTGW on typhoon evolution are investigated based on the simulation results of Typhoon Saomai (2006) using the Weather Research and Forecasting (WRF) model. The power spectral density of HDTGW shows dominant powers at horizontal wavelengths of 20–30 km and at periods of less than one hour. This is associated with gravity waves generated by vigorous convective clouds in an inner core region of the typhoon. However, the domain-averaged HDTGW in the upper troposphere and lower stratosphere had a spectral peak at 24 h, which is well correlated with the minimum sea-level pressure of the typhoon, especially during a rapidly developing period. The 24 h period of the averaged HDTGW stems from the inertia-gravity waves generated by the convective clouds in the spiral rainbands, and showed no clear association with the thermal tides or the diurnal variation of precipitation.

scholarly journals The Impact of Cumulus Parameterization Schemes on the Simulation of Tropical Cyclone “Roanu” Over the Bay of Bengal Using WRF Model

2020 ◽  
Vol 68 (1) ◽  
pp. 87-94
Author(s):  
Saifullah ◽  
Md Idris Ali ◽  
Ashik Imran
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Wrf Model ◽  
Absolute Error ◽  
Sensitivity Study ◽  
Cumulus Parameterization ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Cumulus Parameterization Schemes ◽  
The Impact

A sensitivity study has been made on cumulus parameterization (CP) schemes of Weather Research and Forecasting (WRF) model for the simulation of tropical cyclone Roanu which formed over Bay of Bengal during May 2016. The model was run for 72 hours with different CP schemes such as Kain–Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell–Freit as Ensemble (GFE), Grell 3D Ensemble (G3E) and Grell–Devenyi (GD) Ensemble schemes to study the variation in track, intensity. The landfall position error is minimum for BMJ scheme but the time delayed only 1.5-5 hours for all schemes except GD scheme. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of minimum sea level pressure and maximum wind speed is smaller for BMJ, GFE, GD schemes. The RMSE-MAE of rainfall is minimum for BMJ and G3E schemes. Except GD scheme all the other schemes give the better result. Dhaka Univ. J. Sci. 68(1): 87-94, 2020 (January)

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