scholarly journals A Spatial Pattern Analysis of Land Surface Roughness Heterogeneity and its Relationship to the Initiation of Weak Tornadoes

2019 ◽  
Vol 23 (5) ◽  
pp. 1-28 ◽  
Author(s):  
Amanda Markert ◽  
Robert Griffin ◽  
Kevin Knupp ◽  
Andrew Molthan ◽  
Tim Coleman
Keyword(s):  
Surface Roughness ◽  
Great Plains ◽  
Land Surface ◽  
Spatial Scales ◽  
Land Surface Model ◽  
The United States ◽  
Surface Model ◽  
Spatial Pattern Analysis ◽  
Horizontal Gradients ◽  
Vorticity Vector

Abstract North Alabama is among the most tornado-prone regions in the United States and is composed of more spatially variable terrain and land cover than the frequently studied North American Great Plains region. Because of the high tornado frequency observed across north Alabama, there is a need to understand how land surface roughness heterogeneity influences tornadogenesis, particularly for weak-intensity tornadoes. This study investigates whether horizontal gradients in land surface roughness exist surrounding locations of tornadogenesis for weak (EF0–EF1) tornadoes. The existence of the horizontal gradients could lead to the generation of positive values of the vertical components of the 3D vorticity vector near the surface that may aid in the tornadogenesis process. In this study, surface roughness was estimated using parameterizations from the Noah land surface model with inputs from MODIS 500-m and Landsat 30-m data. Spatial variations in the parameterized roughness lengths were assessed using GIS-based grid and quadrant pattern analyses to quantify observed variation of land surface features surrounding tornadogenesis locations across spatial scales. This analysis determined that statistically significant horizontal gradients in surface roughness exist surrounding tornadogenesis locations.

Model Improvement via Systematic Investigation of Physics Tendencies

2020 ◽  
Vol 148 (2) ◽  
pp. 671-688 ◽  
Author(s):  
May Wong ◽  
Glen Romine ◽  
Chris Snyder
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Land Surface Model ◽  
Warm Season ◽  
The United States ◽  
Systematic Investigation ◽  
Surface Model ◽  
Cumulus Scheme ◽  
Model Improvement ◽  
Precipitation Cycle

Abstract Deficiencies in forecast models commonly stem from inadequate representation of physical processes; yet, improvement to any single physics component within a model may lead to degradations in other physics components or the model as a whole. In this study, a systematic investigation of physics tendencies is demonstrated to help identify and correct compensating sources of model biases. The model improvement process is illustrated by addressing a commonly known issue in warm-season rainfall forecasts from parameterized convection models: the misrepresentation of the diurnal precipitation cycle over land, especially in its timing. Recent advances in closure assumptions in mass-flux cumulus schemes have made remarkable improvements in this respect. Here, we investigate these improvements in the representation of the diurnal precipitation cycle for a spring period over the United States, and how changes to the cumulus scheme impact the model climate and the behavior of other physics schemes. The modified cumulus scheme improves both the timing of the diurnal precipitation cycle and reduces midtropospheric temperature and moisture biases. However, larger temperature and moisture biases are found in the boundary layer as compared to a predecessor scheme, along with an overamplification of the diurnal precipitation cycle, relative to observations. Guided by a tendency analysis, we find that biases in the diurnal amplitude of the precipitation cycle in our simulations, along with temperature and moisture biases in the boundary layer, originate from the land surface model.

scholarly journals A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales

2009 ◽  
Vol 10 (3) ◽  
pp. 577-599 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Sujay V. Kumar ◽  
Charles Alonge ◽  
Wei-Kuo Tao
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Field Experiments ◽  
Climate Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Critical Role ◽  
Land Surface Model ◽  
Surface Model ◽  
Test Bed

Abstract Land–atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed because of the complex interactions and feedbacks present across a range of scales. Furthermore, uncoupled systems or experiments [e.g., the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS)] may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land–atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting Model (WRF) has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The coevolution of these variables and the convective PBL are sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land–atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land–atmosphere coupling (LoCo) using the LIS–WRF system, which will serve as a test bed for future experiments to evaluate coupling diagnostics within the community.

scholarly journals Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation

2012 ◽  
Vol 5 (1) ◽  
pp. 229-276 ◽  
Author(s):  
S. V. Kumar ◽  
C. D. Peters-Lidard ◽  
J. Santanello ◽  
K. Harrison ◽  
Y. Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Evaluation ◽  
Land Surface ◽  
Spatial Scales ◽  
Land Surface Model ◽  
Formal System ◽  
Surface Modeling ◽  
Surface Model ◽  
Diagnostic Model ◽  
Land Surface Modeling

Abstract. Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data

2010 ◽  
Vol 11 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Mutlu Ozdogan ◽  
Matthew Rodell ◽  
Hiroko Kato Beaudoing ◽  
David L. Toll
Keyword(s):  
United States ◽  
Heat Flux ◽  
Land Surface ◽  
Agricultural Land ◽  
Weather Prediction ◽  
Land Surface Model ◽  
The United States ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Ground Heat Flux

Abstract A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land–atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m−2 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m−2, 20 W m−2, 5 mm day−1, 0.3 mm day−1, and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

scholarly journals Diagnosing the Nature of Land–Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U.S. Southern Great Plains

2013 ◽  
Vol 14 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Aaron Kennedy ◽  
Sujay V. Kumar
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Critical Role ◽  
Land Surface Model ◽  
Coupled System ◽  
Surface Model ◽  
Test Bed ◽  
Southern Great Plains ◽  
Pbl Scheme ◽  
The U.S

Abstract Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

scholarly journals Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation

2016 ◽  
Vol 17 (2) ◽  
pp. 669-691 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
The United States ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Temperature

Abstract Multiangle and multipolarization L-band microwave observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated into the Goddard Earth Observing System Model, version 5 (GEOS-5), using a spatially distributed ensemble Kalman filter. A variant of this system is also used for the Soil Moisture Active Passive (SMAP) Level 4 soil moisture product. The assimilation involves a forward simulation of brightness temperatures (Tb) for various incidence angles and polarizations and an inversion of the differences between Tb forecasts and observations into updates to modeled surface and root-zone soil moisture, as well as surface soil temperature. With SMOS Tb assimilation, the unbiased root-mean-square difference between simulations and gridcell-scale in situ measurements in a few U.S. watersheds during the period from 1 July 2010 to 1 July 2014 is 0.034 m3 m−3 for both surface and root-zone soil moisture. A validation against gridcell-scale measurements and point-scale measurements from sparse networks in the United States, Australia, and Europe demonstrates that the assimilation improves both surface and root-zone soil moisture results over the open-loop (no assimilation) estimates in areas with limited vegetation and terrain complexity. At the global scale, the assimilation of SMOS Tb introduces mean absolute increments of 0.004 m3 m−3 to the profile soil moisture content and 0.7 K to the surface soil temperature. The updates induce changes to energy fluxes and runoff amounting to about 15% of their respective temporal standard deviation.

scholarly journals Impact of Irrigation Methods on Land Surface Model Spinup and Initialization of WRF Forecasts

2015 ◽  
Vol 16 (3) ◽  
pp. 1135-1154 ◽  
Author(s):  
Patricia M. Lawston ◽  
Joseph A. Santanello ◽  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Prediction Models ◽  
Land Surface Model ◽  
The United States ◽  
Time Of Day ◽  
Surface Model ◽  
Climate Conditions ◽  
Irrigation Methods ◽  
Time Space

Abstract In the United States, irrigation represents the largest consumptive use of freshwater and accounts for approximately one-third of total water usage. Irrigation impacts soil moisture and can ultimately influence clouds and precipitation through land–planetary boundary layer (PBL) coupling processes. This study utilizes NASA’s Land Information System (LIS) and the NASA Unified Weather Research and Forecasting Model (NU-WRF) framework to investigate the effects of drip, flood, and sprinkler irrigation methods on land–atmosphere interactions, including land–PBL coupling and feedbacks at the local scale. To initialize 2-day, 1-km WRF forecasts over the central Great Plains in a drier-than-normal (2006) and a wetter-than-normal year (2008), 5-yr irrigated LIS spinups were used. The offline and coupled simulation results show that regional irrigation impacts are sensitive to time, space, and method and that irrigation cools and moistens the surface over and downwind of irrigated areas, ultimately resulting in both positive and negative feedbacks on the PBL depending on the time of day and background climate conditions. Furthermore, the results portray the importance of both irrigation method physics and correct representation of several key components of land surface models, including accurate and timely land-cover and crop-type classification, phenology (greenness), and soil moisture anomalies (through a land surface model spinup) in coupled prediction models.

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