The 2024 NOAA Hazardous Weather Testbed Spring Forecasting Experiment What: Over 160 forecasters and researchers convened in-person and virtually to engage in real-time severe weather forecasting and evaluation activities aimed at accelerating research-to-operations and informing NOAA's Unified Forecast System. Major emphases of SFE 2024 included 1) deterministic and ensemble components of the Rapid Refresh Forecast System, 2) the Model for Prediction Across Scales, 3) global artificial intelligence (AI)-based NWP emulators, 4) the Warn-on-Forecast System, and 5) innovative AI-based postprocessing strategies. When: 29 April-31 May 2024 Where: Norman, OK, and Online

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Adam J. Clark, adam.clark@noaa.gov

Characterizing localized climate conditions is becoming important in many aspects of modern society. The Weather Research and Forecasting (WRF) models have been used to predict localized environmental variations. Further, the recently developed Urban Canopy Model (UCM), derived from energy balance equations, represents more detailed urban characteristics, when it is coupled with the WRF model. However, such physics-based numerical models can exhibit a spatially and temporally heterogeneous discrepancy pattern compared to actual climate conditions possibly due to inappropriate model specifications and/or incorrect choices of model parameters. This study devises a new method that post-calibrates geographically- and temporally-varying discrepancy in an integrative framework. Tested on urban temperature data collected in the central Texas region during heat wave events, our case study demonstrates that the proposed method substantially reduces prediction errors over the original WRF/UCM projection and other alternative approaches. Based on the results, we quantify the building energy consumption at spatially dispersed locations. Note to Practitioners—As electricity is a key component of modern society, the economy, and human well-being, the ability to assess the consumption of electricity is increasingly important. This assessment is challenging due to heterogeneous meteorological patterns. While the numerical weather prediction model, such as WRF/UCM, provides localized weather conditions, its output may not be accurate when its parameters and boundary conditions are not properly specified. In particular, we observe the prediction error from WRF/UCM is spatially- and temporally-varying due to the urban heat island effect during heat wave events. This study develops an integrative framework which provides a methodology to post-correct such heterogeneous prediction error. Our case study using data collected in the central Texas region suggests that the energy consumption at highly urbanized areas can be 0.4 kWh-0.5 kWh larger than its surrounding areas during the heat wave periods. The results demonstrate how the proposed approach can benefit a real application use case.

Adam J. Clark2,4, Israel L. Jirak1, Burkely T. Gallo1,3, Kent H. Knopfmeier2,3, Brett Roberts1,2,3, Makenzie Krocak1,3,5, Jake Vancil1,3, Kimberly A. Hoogewind2,3, Nathan A. Dahl1,3, Eric D. Loken2,3,4, David Jahn1,3, David Harrison1,3, David Imy2, Patrick Burke2, Louis J. Wicker2,4, Patrick S. Skinner2,3, Pamela L. Heinselman2,4, Patrick Marsh1, Katie A. Wilson2,3, Andrew R. Dean1, Gerald J. Creager2,3, Thomas A. Jones2,3, Jidong Gao2, Yunheng Wang2,3, Montgomery Flora2,3, Corey K. Potvin2,4, Christopher A. Kerr2,3, Nusrat Yussouf2,3,4, Joshua Martin2,3, Jorge Guerra2,3, Brian C. Matilla2,3, and Thomas J. Galarneau2,3,4

The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques.

In the United States, approximately 40% of the primary energy use and 72% of the electricity use belong to the building sector. This shows the significance of studying the potential for reducing the building energy consumption and buildings' sustainability for ensuring a sustainable development. Therefore, many different efforts focus on reducing the energy consumption of residential buildings. Data-validated building energy modeling methods are among the studies for such an effort, particularly, by enabling the identification of the potential savings associated with different potential retrofit strategies. However, there are many uncertainties that can impact the accuracy of such energy model results, one of which is the weather input data. In this study, to investigate the impact of spatial temperature variation on building energy consumption, six weather stations in an urban area with various urban density were selected. A validated energy model was developed using energy audit data and high-frequency electricity consumption of a residential building in Austin, TX. The energy consumption of the modeled building was compared using the selected six weather datasets. The results show that energy use of a building in an urban area can be impacted by up to 12% due to differences in urban density. This indicates the importance of weather data in predicting energy consumption of the building. The methodology and results of this study can be used by planners and decision makers to reduce uncertainties in estimating the building energy use in urban scale.

attributes of resolved individual storms and storm complexes as proxies of tornado development have been studied. For example, Thompson et al. (2003, 2012) formulated a single parameter that combines values of select environmental attributes that favor tornadic storms, namely the Significant Tornado Parameter (STP). The STP accounts for environmental instability, convective inhibition, wind speed and directional shear, as well as lifting condensation level of the near-storm environment. Favorable tornado environments can be assessed by calculating STP based on CAM forecasts of these fields. Thompson et al. (2017) documented a near-linear relationship among STP values and tornado frequency (Fig. 1) by calculating STP for the near-storm environment of more than 6,500 right-moving supercell storms associated with a severe report (not all tornadic) observed over a two-year period, 2014 – 15.

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.

The Great Plains low-level jet (LLJ) is influential in the initiation and evolution of nocturnal convection through the northward advection of heat and moisture, as well as convergence in the region of the LLJ nose. However, accurate numerical model forecasts of LLJs remain a challenge, related to the performance of the planetary boundary layer (PBL) scheme in the stable boundary layer. Evaluated here using a series of LLJ cases from the Plains Elevated Convection at Night (PECAN) program are modifications to a commonly used local PBL scheme, Mellor–Yamada–Nakanishi–Niino (MYNN), available in the Weather Research and Forecasting (WRF) Model. WRF forecast mean absolute error (MAE) and bias are calculated relative to PECAN rawinsonde observations. The first MYNN modification invokes a new set of constants for the scheme closure equations that, in the vicinity of the LLJ, decreases forecast MAEs of wind speed, potential temperature, and specific humidity more than 19%. For comparison, the Yonsei University (YSU) scheme results in wind speed MAEs 22% lower but specific humidity MAEs 17% greater than in the original MYNN scheme. The second MYNN modification, which incorporates the effects of potential kinetic energy and uses a nonzero mixing length in stable conditions as dependent on bulk shear, reduces wind speed MAEs 66% for levels below the LLJ, but increases MAEs at higher levels. Finally, Rapid Refresh analyses, which are often used for forecast verification, are evaluated here and found to exhibit a relatively large average wind speed bias of 3 m s−1 in the region below the LLJ, but with relatively small potential temperature and specific humidity biases.

The viability of wind-energy generation is dependent on highly accurate numerical wind forecasts, which are impeded by inaccuracies in model representation of boundary-layer processes. This study revisits the basic theory of the Mellor, Yamada, Nakanishi, and Niino (MYNN) planetary boundary-layer parametrization scheme, focusing on the onset of wind-ramp events related to nocturnal low-level jets. Modifications to the MYNN scheme include: (1) calculation of new closure parameters that determine the relative effects of turbulent energy production, dissipation, and redistribution; (2) enhanced mixing in the stable boundary layer when the mean wind speed exceeds a specified threshold; (3) explicit accounting of turbulent potential energy in the energy budget. A mesoscale model is used to generate short-term (24 h) wind forecasts for a set of 15 cases from both the U.S.A. and Germany. Results show that the new set of closure parameters provides a marked forecast improvement only when used in conjunction with the new mixing length formulation and only for cases that are originally under- or over-forecast (10 of the 15 cases). For these cases, the mean absolute error (MAE) of wind forecasts at turbine-hub height is reduced on average by 17%. A reduction in MAE values on average by 26% is realized for these same cases when accounting for the turbulent potential energy together with the new mixing length. This last method results in an average reduction by at least 13% in MAE values across all 15 cases.

Wind ramps are relatively large changes in wind speed over a period of a few hours and present a challenge for electric utilities to balance power generation and load. Failures of boundary-layer parametrization schemes to represent physical processes limit the ability of numerical models to forecast wind ramps, especially in a stable boundary layer. Herein, the eight “closure parameters” of a widely used boundary-layer parameterization scheme are subject to sensitivity tests for a set of wind-ramp cases. A marked sensitivity of forecast wind speed to closure-parameter values is observed primarily for three parameters that influence in the closure equations the depth of turbulent mixing, dissipation, and the transfer of kinetic energy from the mean to the turbulent flow. Reducing the value of these parameters independently by 25% or by 50% reduces the overall average in forecast wind-speed errors by at least 24% for the first two parameters and increases average forecast error by at least 63% for the third parameter. Doubling any of these three parameters increases average forecast error by at least 67%. Such forecast sensitivity to closure parameter values provides motivation to explore alternative values in the context of a stable boundary layer.

The design of an interregional high-voltage transmission system in the US is a revolutionary technological concept that will likely play a significant role in the planning and operation of future electric power systems. Historically, the primary justification for building interregional high-voltage transmission lines in the US and around the world has been based on economic and reliability criteria. Today, the implementation renewable portfolio standards, carbon emission regulations, the improvements in the performance of power electronic systems, and unused benefits associated with capacity exchange during times of non-coincident peak demand, are driving the idea of designing an interregional high-voltage transmission system in the US. However, there exist challenges related to technical, economic, public policy, and environmental factors that hinder the implementation of such a complex infrastructure. The natural skepticism from many sectors of the society, in regards to how will the system be operated, how much will it cost, and the environmental impact that it could potentially create are among the most significant challenges to its rapid implementation. This publication aims at illustrating the technological, environmental, economic, and policy challenges that interregional HV transmission systems face today in the US, looking specifically at the Clean Line Rock Island project in Iowa.

The lack of accurate moisture/cloud initial conditions is one of the major causes for the spinup problem in explicit cloud and precipitation forecasting models during the first a few hours. Although many studies have sought a remedy by using satellite or/and radar data, the lack of detailed information on initial moisture, cloud water and latent heating fields is still a key problem. NEXRAD data can provide the three-dimensional precipitation field with high spatial and temporal resolution, though in a model, other conventional variables (i.e., water vapor, tempera- ture and wind) may not be consistent with the cloud and precipitation analysis fields. Thus, evaporation pro- cesses may quickly kill convective storms present at the start of the model forecast. To address this problem, a diabatic initialization scheme has been improved to provide a latent heat forc- ing in the model thermodynamic equation and to force vertical circulations and the associated divergence that is consistent with the observed precipitation. The 28 March 2000 Fort Worth/Texas tornado storm was chosen to explore options for applying the diabatic initialization technique, which essentially involves forcing the model over some time period with a heating field based upon NIDS (NEXRAD Information Dissemination Service, Baer, 1991) radar reflectivity, using an intermittent dia- batic assimilation (IDA) technique.

The lack of accurate moisture/cloud initial conditions is one of the major causes for the spinup problem in explicit cloud and precipitation forecasting models during the first a few hours. Although many studies have sought a remedy by using satellite or/and radar data, the lack of detailed information on initial moisture, cloud water and latent heating fields is still a key problem. NEXRAD data can provide the three-dimensional precipitation field with high spatial and temporal resolution, though in a model, other conventional variables (i.e., water vapor, tempera- ture and wind) may not be consistent with the cloud and precipitation analysis fields. Thus, evaporation pro- cesses may quickly kill convective storms present at the start of the model forecast. To address this problem, a diabatic initialization scheme has been improved to provide a latent heat forc- ing in the model thermodynamic equation and to force vertical circulations and the associated divergence that is consistent with the observed precipitation. The 28 March 2000 Fort Worth/Texas tornado storm was chosen to explore options for applying the diabatic initialization technique, which essentially involves forcing the model over some time period with a heating field based upon NIDS (NEXRAD Information Dissemination Service, Baer, 1991) radar reflectivity, using an intermittent dia- batic assimilation (IDA) technique. 2. INTERMITTENT DIABATIC ASSIMILATION (IDA)