Mesoscale Ensemble Weather Prediction at U.S. Army Dugway Proving Ground, Utah

Knievel, Jason C.; Liu, Yubao; Hopson, T. M.; Shaw, Justin S; Halvorson, Scott F.; Fisher, H.H.; Roux, Gregory; Sheu, Rong Shyang; Pan, Linlin; Wu, Wanli; Hacker, Joshua P.; Vernon, Eric; Gallagher, Frank W.; Pace, J.C.

doi:10.1175/waf-d-17-0049.1

Cited by 10 publications

(7 citation statements)

References 79 publications

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“…Surface variables (e.g., wind) can also be sensitive to the land surface model, land use and topography [24][25][26][27][28]. Data assimilation has an impact on atmospheric or oceanic models in general, and it also plays an important role in WRF model simulations [29]; post-processing can also improve the results [30]. However, further studies of the sensitivities of the different schemes, particularly those that are newly implemented, are needed.…”

Section: Introductionmentioning

confidence: 99%

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Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

et al. 2018

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This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5-0.8 K; the 2-m specific humidity has biases from −0.5-−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2-1.18 m/s and −0.5-4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.

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Section: Introductionmentioning

confidence: 99%

“…In this study, 48 experiments were conducted with an ensemble prediction system (EPS) [30]. Each member of the ensemble is treated as a different experiment to investigate model sensitivities to different configurations.…”

Section: Introductionmentioning

confidence: 99%

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

et al. 2018

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“…The DSS process (Figure 1) starts with the selection of the most appropriate EOS data for precipitation (P) and evapotranspiration (ET) variables for the selected domain (EOM). The selected P dataset is then used to calibrate ensemble weather and climate forecasts using combined analog and quantile regression approaches (Koenker and Bassett 1978;Hopson and Webster 2010;Hopson 2014;Knievel et al 2017) at the daily to seasonal time scales in the CFM. An additional forecast calibration methodology in the form of Bayesian Model Averaging (BMA) (Hoeting et al 1999;Raftery et al 2005) is also implemented.…”

Section: Methodsmentioning

confidence: 99%

“…The combined analog and quantile regression algorithm (Koenker and Bassett 1978;Knievel et al 2017) can be explained as follows. Let {y i } represent a set of observations of the regressand y of interest, and {x i } be an associated set of predictor values.…”

Section: Climate Forecasting Modulementioning

confidence: 99%

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

Koppa

Gebremichael

Hopson

et al. 2021

J American Water Resour Assoc

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A novel decision support system based on earth observation satellites, ensemble climate forecasts and hydrologic models for hydropower planning under deep data uncertainty in Africa.ABSTRACT: Hydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite-based datasets is overcome by using a data validation framework which does not require ground-based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero-skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

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“…Weather Research and Forecasting (WRF) model to predict the weather conditions at the US Army Test and Evaluation Command (ATEC) Ranges (Knievel et al, 2017;Liu et al, 2008). The system is a product of collaboration between ATEC and NCAR.…”

Section: Campaign Meteorological Conditionsmentioning

confidence: 99%

Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) Campaign: High-Resolution in situ Observations above the Nocturnal Boundary Layer

Doddi

Lawrence

Fritts

et al. 2021

Preprint

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Abstract. The Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) project was conceived to improve our understanding of the dynamics of sheet and layer (S&L) structures in the lower troposphere under strongly stable conditions. The approach employed a synergistic combination of targeted multi-point observations using small unmanned aircraft systems (sUAS) guiding direct numerical simulation (DNS) modeling to characterize the dynamics driving the S&L structures and associated flow features. The IDEAL research program consisted of two phases. The first was an observational field campaign to systematically probe stable lower atmosphere conditions using multiple DataHawk-2 (DH2) sUAS. Coordinated, simultaneous multi-DH2 flights were guided by concurrent Integrated Sounding System (ISS) wind profiler radar and radiosonde soundings performed by NCAR Earth Observing Laboratory (EOL) participants. Additional sUAS flight guidance was obtained from real-time sUAS measurements. Following the field campaign, the second phase focused on high-resolution DNS modeling efforts guided by in-situ observations made during the first phase. This overview focuses on the details of the observational phase that took place from 24 October to 15 November 2017 at Dugway Proving Ground (DPG), Utah. A total of 72 DH2 flights coordinated with 93 balloon-borne radiosondes were deployed in support of the IDEAL field campaign. Our discussion addresses the average atmospheric conditions, the observation strategy, and the objectives of the field campaign. Also presented are representative flight sorties and sUAS environmental and turbulence measurements.

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Mesoscale Ensemble Weather Prediction at U.S. Army Dugway Proving Ground, Utah

Cited by 10 publications

References 79 publications

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) Campaign: High-Resolution in situ Observations above the Nocturnal Boundary Layer

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