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 Simulation of Monsoon Low Pressure System and its Associated Rainfall Over Bangladesh Using WRF Model

2018 ◽  
Vol 66 (1) ◽  
pp. 29-35
Author(s):  
Deepa Roy ◽  
Md Abdus Samad ◽  
SM Quamrul Hassan
Keyword(s):  
Land Surface ◽  
Wrf Model ◽  
Vertical Wind Shear ◽  
Low Pressure ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Pressure System ◽  
Hourly Data ◽  
Low Pressure System

In this paper an effort has been made to simulate the monsoon Low Pressure System (LPS) and its associated rainfall event of 16-20 August, 2013 using Weather Research and Forecasting (WRF) model. The model was run for 24-h, 48-h and 72-h in a single domain of 10 km horizontal resolution using The National Centre for Environmental Prediction (NCEP) high-resolution Global Final (FNL) Analysis 6-hourly data using initial and lateral boundary conditions. WRF double-moment 5 class micro physics scheme, Kain–Fritsch (new Eta) cumulus physics scheme,Yonsei University planetary boundary layer scheme, Revised MM5 surface layer physics scheme, Unified Noah LSM as land surface model, Rapid Radiative Transfer Model (RRTM) for long-wave and Dudhia scheme for short-wave scheme are used for the simulation. The performance of the model is evaluated analyzing Mean Sea Level Pressure (MSLP), Wind Pattern, Vorticity, Vertical Wind Shear and Rainfall Distribution. The model successfully captured the low pressure system, initial condition, propagation, landfall time and location reasonably well. The model simulated rainfall amount and associated areas sensibly well compared with the observed data by BMD and Tropical Rainfall Measuring Mission (TRMM). Dhaka Univ. J. Sci. 66(1): 29-35, 2018 (January)

scholarly journals Global Calibration of the GEOS-5 L-Band Microwave Radiative Transfer Model over Nonfrozen Land Using SMOS Observations

2013 ◽  
Vol 14 (3) ◽  
pp. 765-785 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Land Surface ◽  
Radiative Transfer Model ◽  
Absolute Difference ◽  
Surface Model ◽  
Transfer Model ◽  
Global Average ◽  
L Band ◽  
Parameter Values

Abstract A zero-order (tau-omega) microwave radiative transfer model (RTM) is coupled to the Goddard Earth Observing System, version 5 (GEOS-5) catchment land surface model in preparation for the future assimilation of global brightness temperatures (Tb) from the L-band (1.4 GHz) Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Simulations using literature values for the RTM parameters result in Tb biases of 10–50 K against SMOS observations. Multiangular SMOS observations during nonfrozen conditions from 1 July 2011 to 1 July 2012 are used to calibrate parameters related to the microwave roughness h, vegetation opacity τ and/or scattering albedo ω separately for each observed 36-km land grid cell. A particle swarm optimization is used to minimize differences in the long-term (climatological) mean values and standard deviations between SMOS observations and simulations, without attempting to reduce the shorter-term (seasonal to daily) errors. After calibration, global Tb simulations for the validation year (1 July 2010 to 1 July 2011) are largely unbiased for multiple incidence angles and both H and V polarization [e.g., the global average absolute difference is 2.7 K for TbH(42.5°), i.e., at 42.5° incidence angle]. The calibrated parameter values depend to some extent on the specific land surface conditions simulated by the GEOS-5 system and on the scale of the SMOS observations, but they also show realistic spatial distributions. Aggregating the calibrated parameter values by vegetation class prior to using them in the RTM maintains low global biases but increases local biases [e.g., the global average absolute difference is 7.1 K for TbH(42.5°)].

scholarly journals A Fast Visible Wavelength 3-D Radiative Transfer Procedure for NWP Visualization and Forward Modeling

2019 ◽  
Author(s):  
Steven Albers ◽  
Stephen M. Saleeby ◽  
Sonia Kreidenweis ◽  
Qijing Bian ◽  
Peng Xian ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Transfer ◽  
Land Surface ◽  
Light Propagation ◽  
Situational Awareness ◽  
Initial Conditions ◽  
Lower Boundary ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Visible Wavelength

Abstract. Solar radiation is the ultimate source of energy for all atmospheric motions. The visible wavelength range of solar radiation represents a significant contribution to the Earth’s energy budget and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast and physically based visible wavelength 3-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, and precipitating rain, snow, or ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360° spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the Earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWim imagery can also be generated for and compared with observed camera images to (i) assess the fidelity, (ii) and improve the performance of numerical atmospheric and land surface models, as well as (iii) through their inclusion into an observational data assimilation scheme, improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and NWP forecast initialization applications.

Hyper-resolution land surface modeling enables 30-m SMAP-based soil moisture at continental scales

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Nathaniel W. Chaney ◽  
Hylke E. Beck ◽  
Ming Pan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Field Scale ◽  
Remotely Sensed Data

<p>Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km to 100 m). Although these techniques yield aesthetically pleasing maps, by neglecting how the water and energy fluxes physically interact with the landscape, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.</p><p>This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission and subsequently merging the HydroBlocks-RTM and the SMAP L3-enhanced brightness temperature at the HRU scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains. </p><p>We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2018). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales, outperforming both the SMAP L3-enhanced and the SMAP L4 soil moisture products. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals and highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. </p>

Will Perturbing Soil Moisture Improve Warm-Season Ensemble Forecasts? A Proof of Concept

2006 ◽  
Vol 134 (11) ◽  
pp. 3174-3189 ◽  
Author(s):  
Christian Sutton ◽  
Thomas M. Hamill ◽  
Thomas T. Warner
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Short Range ◽  
Wrf Model ◽  
Warm Season ◽  
Ensemble Forecast ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Convective Parameterization

Abstract Current generation short-range ensemble forecast members tend to be unduly similar to each other, especially for components such as surface temperature and precipitation. One possible cause of this is a lack of perturbations to the land surface state. In this experiment, a two-member ensemble of the Advanced Research Weather Research and Forecasting (WRF) model (ARW) was run from two different soil moisture analyses. One-day forecasts were conducted for six warm-season cases over the central United States with moderate soil moistures, both with explicit convection at 5-km grid spacing and with parameterized convection at 20-km grid spacing. Since changing the convective parameterization has previously been demonstrated to cause significant differences between ensemble forecast members, 20-km simulations were also conducted that were initialized with the same soil moisture but that used two different convective parameterizations as a reference. At 5 km, the forecast differences due to changing the soil moisture were comparable to the differences in 20-km simulations with the same soil moisture but with a different convective parameterization. The differences of 20-km simulations from different soil moistures were occasionally large but typically smaller than the differences from changing the convective parameterization. Thus, perturbing the state of the land surface for this version of WRF/ARW was judged to be likely to increase the spread of warm-season operational short-range ensemble forecasts of precipitation and surface temperature when soil moistures are moderate in value, especially if the ensemble is comprised of high-resolution members with explicit convection.

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 Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

2015 ◽  
Vol 16 (3) ◽  
pp. 1109-1134 ◽  
Author(s):  
H. Lievens ◽  
A. Al Bitar ◽  
N. E. C. Verhoest ◽  
F. Cabot ◽  
G. J. M. De Lannoy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Land Surface ◽  
Microwave Emission ◽  
Cumulative Distribution ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
The Impact ◽  
Forward Operator

Abstract The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

scholarly journals Evaluation of Parameterization Schemes in the WRF Model for Estimation of Mixing Height

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
R. Shrivastava ◽  
S. K. Dash ◽  
R. B. Oza ◽  
D. N. Sharma
Keyword(s):  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
India Meteorological Department ◽  
Cold Season ◽  
Surface Model ◽  
Mixing Height ◽  
Parameterization Schemes ◽  
Pbl Scheme ◽  
Noah Land Surface Model

This paper deals with the evaluation of parameterization schemes in the WRF model for estimation of mixing height. Numerical experiments were performed using various combinations of parameterization schemes and the results were compared with the mixing height estimated using the radiosonde observations taken by the India Meteorological Department (IMD) at Mangalore site for selected days of the warm and cold season in the years 2004–2007. The results indicate that there is a large variation in the mixing heights estimated by the model using various combinations of parameterization schemes. It was seen that the physics option consisting of Mellor Yamada Janjic (Eta) as the PBL scheme, Monin Obukhov Janjic (Eta) as the surface layer scheme, and Noah land surface model performs reasonably well in reproducing the observed mixing height at this site for both the seasons as compared to the other combinations tested. This study also showed that the choice of the land surface model can have a significant impact on the simulation of mixing height by a prognostic model.

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.

scholarly journals Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions

2017 ◽  
Vol 200 ◽  
pp. 101-120 ◽  
Author(s):  
C. E. Scott ◽  
S. A. Monks ◽  
D. V. Spracklen ◽  
S. R. Arnold ◽  
P. M. Forster ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Surface ◽  
Transport Model ◽  
Radiative Transfer Model ◽  
Change Scenario ◽  
Surface Model ◽  
Transfer Model ◽  
Radiative Effect ◽  
Radiative Effects

More than one quarter of natural forests have been cleared by humans to make way for other land-uses, with changes to forest cover projected to continue. The climate impact of land-use change (LUC) is dependent upon the relative strength of several biogeophysical and biogeochemical effects. In addition to affecting the surface albedo and exchanging carbon dioxide (CO2) and moisture with the atmosphere, vegetation emits biogenic volatile organic compounds (BVOCs), altering the formation of short-lived climate forcers (SLCFs) including aerosol, ozone (O3) and methane (CH4). Once emitted, BVOCs are rapidly oxidised by O3, and the hydroxyl (OH) and nitrate (NO3) radicals. These oxidation reactions yield secondary organic products which are implicated in the formation and growth of aerosol particles and are estimated to have a negative radiative effect on the climate (i.e. a cooling). These reactions also deplete OH, increasing the atmospheric lifetime of CH4, and directly affect concentrations of O3; the latter two being greenhouse gases which impose a positive radiative effect (i.e. a warming) on the climate. Our previous work assessing idealised deforestation scenarios found a positive radiative effect due to changes in SLCFs; however, since the radiative effects associated with changes to SLCFs result from a combination of non-linear processes it may not be appropriate to scale radiative effects from complete deforestation scenarios according to the deforestation extent. Here we combine a land-surface model, a chemical transport model, a global aerosol model, and a radiative transfer model to assess the net radiative effect of changes in SLCFs due to historical LUC between the years 1850 and 2000.

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