scholarly journals Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India

2010 ◽  
Vol 28 (2) ◽  
pp. 603-619 ◽  
Author(s):  
M. Rajeevan ◽  
A. Kesarkar ◽  
S. B. Thampi ◽  
T. N. Rao ◽  
B. Radhakrishna ◽  
...  
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Surface Wind ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Severe Thunderstorm ◽  
Severe Convection ◽  
Wind Measurements ◽  
Simulated Surface ◽  
Southeast India

Abstract. In the present study, we have used the Weather Research and Forecasting (WRF) model to simulate the features associated with a severe thunderstorm observed over Gadanki (13.5° N, 79.2° E), over southeast India, on 21 May 2008 and examined its sensitivity to four different microphysical (MP) schemes (Thompson, Lin, WSM6 and Morrison). We have used the WRF model with three nested domains with the innermost domain of 2 km grid spacing with explicit convection. The model was integrated for 36 h with the GFS initial conditions of 00:00 UTC, 21 May 2008. For validating simulated features of the thunderstorm, we have considered the vertical wind measurements made by the Indian MST radar installed at Gadanki, reflectivity profiles by the Doppler Weather Radar at Chennai, and automatic weather station data at Gadanki. There are major differences in the simulations of the thunderstorm among the MP schemes, in spite of using the same initial and boundary conditions and model configuration. First of all, all the four schemes simulated severe convection over Gadanki almost an hour before the observed storm. The DWR data suggested passage of two convective cores over Gadanki on 21 May, which was simulated by the model in all the four MP schemes. Comparatively, the Thompson scheme simulated the observed features of the updraft/downdraft cores reasonably well. However, all the four schemes underestimated strength and vertical extend of the updraft cores. The MP schemes also showed problems in simulating the downdrafts associated with the storm. While the Thompson scheme simulated surface rainfall distribution closer to observations, the other three schemes overestimated observed rainfall. However, all the four MP schemes simulated the surface wind variations associated with the thunderstorm reasonably well. The model simulated reflectivity profiles were consistent with the observed reflectivity profile, showing two convective cores. These features are consistent with the simulated condensate profiles, which peaked around 5–6 km. As the results are dependent on initial conditions, in simulations with different initial conditions, different schemes may become closer to observations. The present study suggests not only large sensitivity but also variability of the microphysical schemes in the simulations of the thunderstorm. The study also emphasizes the need for a comprehensive observational campaign using multi-observational platforms to improve the parameterization of the cloud microphysics and land surface processes over the Indian region.

scholarly journals Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Sujata Pattanayak ◽  
U. C. Mohanty ◽  
Krishna K. Osuri
Keyword(s):  
Land Surface ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Surface Wind ◽  
India Meteorological Department ◽  
Heat Fluxes ◽  
Cumulus Convection ◽  
Cyclonic Storm ◽  
Parameterization Schemes ◽  
Cyclone Nargis

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

scholarly journals Evaluation of the Sensitivity of Medicane Ianos to Model Microphysics and Initial Conditions Using Satellite Measurements

2021 ◽  
Vol 13 (24) ◽  
pp. 4984
Author(s):  
Albert Comellas Prat ◽  
Stefano Federico ◽  
Rosa Claudia Torcasio ◽  
Leo Pio D’Adderio ◽  
Stefano Dietrich ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Surface Wind ◽  
Wrf Model ◽  
The Other ◽  
Mature Stage ◽  
Dual Frequency ◽  
Satellite Measurements ◽  
Microphysics Scheme ◽  
Mature Phase ◽  
Sea Surface Wind

Tropical-like cyclone (TLC or medicane) Ianos formed during mid-September 2020 over the Southern Mediterranean Sea, and, during its mature stage on days 17–18, it affected southern Italy and especially Greece and its Ionian islands, where it brought widespread disruption due to torrential rainfall, severe wind gusts, and landslides, causing casualties. This study performs a sensitivity analysis of the mature phase of TLC Ianos with the WRF model to different microphysics parameterization schemes and initial and boundary condition (IBC) datasets. Satellite measurements from the Global Precipitation Measurement Mission-Core Observatory (GPM-CO) dual-frequency precipitation radar (DPR) and the Advanced Scatterometer (ASCAT) sea-surface wind field were used to verify the WRF model forecast quality. Results show that the model is most sensitive to the nature of the IBC dataset (spatial resolution and other dynamical and physical differences), which better defines the primary mesoscale features of Ianos (low-level vortex, eyewall, and main rainband structure) when using those at higher resolution (~25 km versus ~50 km) independently of the microphysics scheme, but with the downside of producing too much convection and excessively low minimum surface pressures. On the other hand, no significant differences emerged among their respective trajectories. All experiments overestimated the vertical extension of the main rainbands and display a tendency to shift the system to the west/northwest of the actual position. Especially among the experiments with the higher-resolution IBCs, the more complex WRF microphysics schemes (Thompson and Morrison) tended to outperform the others in terms of rain rate forecast and most of the other variables examined. Furthermore, WSM6 showed a good performance while WDM6 was generally the least accurate. Lastly, the calculation of the cyclone phase space diagram confirmed that all simulations triggered a warm-core storm, and all but one also exhibited axisymmetry at some point of the studied lifecycle.

scholarly journals Genesis of Tibetan Plateau Vortex: Roles of Surface Diabatic and Atmospheric Condensational Latent Heating

2019 ◽  
Vol 58 (12) ◽  
pp. 2633-2651 ◽  
Author(s):  
Feimin Zhang ◽  
Chenghai Wang ◽  
Zhaoxia Pu
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Diabatic Heating ◽  
Vertical Component ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Lower Atmosphere ◽  
Positive Contribution ◽  
Simulation Performance ◽  
Tibetan Plateau Vortex

AbstractNumerical simulations of a nighttime-generated Tibetan Plateau vortex (TPV) are conducted using the advanced Weather Research and Forecasting (WRF) Model. It is found that the nighttime TPV forms as a result of the merging of convections. Although the WRF Model can reproduce the genesis of the nighttime TPV well, colder and drier biases in the lower atmosphere and drier biases in the upper atmosphere are still presented, thus degrading the simulation performance. Intercomparisons among the experiments indicate that the simulations are more sensitive to land surface schemes than to cloud microphysics schemes. The development of convection is more favorable when daytime surface diabatic heating is vigorous. Surface diabatic heating during daytime plays a dominant role in the development of daytime convection and the genesis of nighttime TPV. Further diagnosis of the PV budget reveals that the obvious increase in PV in the lower atmosphere is associated with the evidently strengthened cyclonic vorticity during TPV genesis. This could be attributed to the increased vertical component of net cross-boundary PV fluxes during the merging of convections as well as the significant positive contribution of diabatic heating effects in the lower atmosphere. Therefore, strong daytime surface diabatic heating, which is essential to convection development, could provide a favorable condition for nighttime TPV genesis. Overall results illuminate the complicated process of TPV genesis.

scholarly journals Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme

2016 ◽  
Vol 144 (3) ◽  
pp. 833-860 ◽  
Author(s):  
Yue Zheng ◽  
Kiran Alapaty ◽  
Jerold A. Herwehe ◽  
Anthony D. Del Genio ◽  
Dev Niyogi
Keyword(s):  
High Resolution ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Cumulus Parameterization ◽  
Subgrid Scale ◽  
Grid Spacing ◽  
Dynamic Adjustment ◽  
Surface Precipitation

Abstract Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency. A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

scholarly journals Linkages between land initialization of the NASA-Unified WRF v7 and biogenic isoprene emission estimates during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Temperature Fields ◽  
Surface Model ◽  
Near Surface ◽  
Isoprene Emission

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that initializing the Noah land surface model directly using a coarser resolution dataset North American Regional Reanalysis (NARR) led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7)'s (near-) surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing the land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF's surface air temperature fields by ~ 2 °C. We also show that the LIS land initialization can modify the surface air temperature errors almost ten times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the NARR-initialized NUWRF run, and are closer to the aircraft observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified errors on small scales, possibly resulted from some limitations of MEGAN's parameterization and its inputs' uncertainty. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling, which we anticipate to be also critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

scholarly journals Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts

2006 ◽  
Vol 134 (9) ◽  
pp. 2632-2641 ◽  
Author(s):  
William A. Gallus ◽  
James F. Bresch
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Wrf Model ◽  
Warm Season ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Grid Spacing ◽  
Pbl Scheme ◽  
Convective Scheme

Abstract A series of simulations for 15 events occurring during August 2002 were performed using the Weather Research and Forecasting (WRF) model over a domain encompassing most of the central United States to compare the sensitivity of warm season rainfall forecasts with changes in model physics, dynamics, and initial conditions. Most simulations were run with 8-km grid spacing. The Advanced Research WRF (ARW) and the nonhydrostatic mesoscale model (NMM) dynamic cores were used. One physics package (denoted NCEP) used the Betts–Miller–Janjic convective scheme with the Mellor–Yamada–Janjic planetary boundary layer (PBL) scheme and GFDL radiation package; the other package (denoted NCAR) used the Kain–Fritsch convective scheme with the Yonsei University PBL scheme and the Dudhia rapid radiative transfer model radiation. Other physical schemes were the same (e.g., the Noah land surface model, Ferrier et al. microphysics) in all runs. Simulations suggest that the sensitivity of the model to changes in physics is a function of which the dynamic core is used, and the sensitivity to the dynamic core is a function of the physics used. The greatest sensitivity in general is associated with a change in physics packages when the NMM core is used. Sensitivity to a change in physics when the ARW core is used is noticeably less. For light rainfall, the spread in the rainfall forecasts when physics are changed under the ARW core is actually less at most times than when the dynamic core is changed while NCAR physics are used. For light rainfall, the WRF model using NCAR physics is much more sensitive to a change in dynamic core than the WRF model using NCEP physics. For heavier rainfall, the opposite is true with a greater sensitivity occurring when NCEP physics is used. Sensitivity to initial conditions (Eta versus the Rapid Update Cycle with an accompanying small change in grid spacing) is generally less substantial than the sensitivity to changes in dynamic core or physics, except in the first 6–12 h of the forecast when it is comparable. As might be expected for warm season rainfall, the finescale structure of rainfall forecasts is more affected by the physics used than the dynamic core used. Surprisingly, however, the overall areal coverage and rain volume within the domain may be more influenced by the dynamic core choice than the physics used.

scholarly journals WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables

2010 ◽  
Vol 138 (8) ◽  
pp. 3342-3355 ◽  
Author(s):  
Juan J. Ruiz ◽  
Celeste Saulo ◽  
Julia Nogués-Paegle
Keyword(s):  
Soil Moisture ◽  
South America ◽  
Land Surface ◽  
Surface Wind ◽  
Model Performance ◽  
Wrf Model ◽  
Land Surface Model ◽  
Forecast Model ◽  
South Atlantic Convergence Zone ◽  
Surface Model

Abstract The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models.

scholarly journals Impact of Soil Moisture Assimilation on Land Surface Model Spinup and Coupled Land–Atmosphere Prediction

2016 ◽  
Vol 17 (2) ◽  
pp. 517-540 ◽  
Author(s):  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Patricia M. Lawston
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Satellite Monitoring ◽  
Surface Model ◽  
The Impact

Abstract Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASA’s Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land–atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS–WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spinup can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux–PBL–ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

2020 ◽  
Vol 35 (3) ◽  
pp. 891-919 ◽  
Author(s):  
Ricardo Fonseca ◽  
Marouane Temimi ◽  
Mohan Satyanarayana Thota ◽  
Narendra Reddy Nelli ◽  
Michael John Weston ◽  
...  
Keyword(s):  
Wind Speed ◽  
Eddy Covariance ◽  
United Arab Emirates ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Surface Drag ◽  
Near Surface

Abstract The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

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