Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations

Blaylock, Brian K.; Horel, John D.

doi:10.1175/waf-d-19-0141.1

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…Fractions skill score (FSS) is a spatial verification method considering the spatial displacement and bias (Blaylock & Horel, 2020; Mittermaier, 2021; Roberts & Lean, 2008). FSS is defined as one minus the fractional area mean square error at the grid points within a neighbourhood divided by the maximum possible mean square error in that neighbourhood (Roberts & Lean, 2008; Wolff et al ., 2014).…”

Section: Methodsmentioning

confidence: 99%

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

Hari Prasad,

Prasad,

Sateesh

et al. 2023

Quart J Royal Meteoro Soc

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The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.

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Section: Methodsmentioning

confidence: 99%

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

Hari Prasad,

Prasad,

Sateesh

et al. 2023

Quart J Royal Meteoro Soc

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“…The High-Resolution Rapid Refresh (HRRR) model (Dowell et al, 2022) output is archived in grib2 format at the University of Utah https://home.chpc. utah.edu/∼u0553130/Brian_Blaylock/hrrr_FAQ.html (Blaylock & Horel, 2021;B. K. Blaylock et al, 2017).…”

Section: Data Availability Statementmentioning

confidence: 99%

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

Turney,

Saide,

Jimenez Munoz

et al. 2023

JGR Atmospheres

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Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

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“…In the past, the weather prediction data are updated only a few times a day, this model is usually suitable for relatively long-term wind power prediction (Allen et al, 2017). In the recent years, the High-Resolution Rapid Refresh (HRRR) system (Blaylock and Horel, 2020) and IBM global high-resolution atmospheric forecasting system (IBM GRAF) models are developed. HRRR v4 will be released in June 2020.…”

Section: The Deterministic Prediction Of Wind Powermentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

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With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

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Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations

Cited by 7 publications

References 49 publications

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

A state-of-the-art review on wind power deterministic prediction

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