Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.

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HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Remote Sensing ◽

10.3390/rs13112179 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2179

Author(s):

Pedro Mateus ◽

Virgílio B. Mendes ◽

Sandra M. Plecha

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Real Time ◽

Prediction Models ◽

Weather Prediction ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

International Gnss Service ◽

Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

Monthly Weather Review ◽

10.1175/mwr-d-17-0039.1 ◽

2017 ◽

Vol 145 (12) ◽

pp. 4911-4936 ◽

Cited By ~ 13

Author(s):

Jonathan Labriola ◽

Nathan Snook ◽

Youngsun Jung ◽

Bryan Putnam ◽

Ming Xue

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Radar Data ◽

Ensemble Forecasts ◽

South Central ◽

Single Moment ◽

Double Moment ◽

Horizontal Grid ◽

Severe Weather Event ◽

Numerical Weather Prediction Models

Explicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size of hail (MESH), hydrometeor classification algorithm (HCA) output, and hail size discrimination algorithm (HSDA) output. Resulting hail forecasts show at most marginal skill, with the level of skill dependent on the forecast initialization time and microphysical scheme used. Forecasts using the double-moment scheme predict many small hailstones aloft, while the single-moment members predict larger hailstones. Near the surface, double-moment members predict larger hailstone sizes than their single-member counterparts. Hail in the forecasts is found to melt too quickly near the surface for members using either of the microphysics schemes examined. Analysis of microphysical budgets in both schemes indicates that both schemes suboptimally represent hail processes, adversely impacting the skill of surface hail forecasts.

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The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

Monthly Weather Review ◽

10.1175/mwr-d-11-00181.1 ◽

2012 ◽

Vol 140 (8) ◽

pp. 2689-2705 ◽

Cited By ~ 29

Author(s):

Marc Berenguer ◽

Madalina Surcel ◽

Isztar Zawadzki ◽

Ming Xue ◽

Fanyou Kong

Keyword(s):

Diurnal Cycle ◽

Prediction Models ◽

Numerical Models ◽

Weather Prediction ◽

Model Performance ◽

Radar Data ◽

Skill Scores ◽

Diurnal Cycle Of Precipitation ◽

Timing Of Initiation ◽

Short Term Forecasting

Abstract This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting. The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar. Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill. The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 103°–85°W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

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Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

Weather and Forecasting ◽

10.1175/waf-d-17-0145.1 ◽

2018 ◽

Vol 33 (2) ◽

pp. 599-607 ◽

Cited By ~ 20

Author(s):

John R. Lawson ◽

John S. Kain ◽

Nusrat Yussouf ◽

David C. Dowell ◽

Dustan M. Wheatley ◽

...

Keyword(s):

Data Assimilation ◽

Real Time ◽

Initial Conditions ◽

Spatial Scales ◽

Weather Prediction ◽

Radar Data ◽

Model Systems ◽

Deterministic Systems ◽

Forecast Lead Time ◽

The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

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Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0163.1 ◽

2012 ◽

Vol 51 (10) ◽

pp. 1835-1854 ◽

Cited By ~ 9

Author(s):

Jure Cedilnik ◽

Dominique Carrer ◽

Jean-François Mahfouf ◽

Jean-Louis Roujean

Keyword(s):

Land Surface ◽

Short Range ◽

Surface Albedo ◽

Weather Prediction ◽

A Priori ◽

Limited Area ◽

Model Biases ◽

Nwp Model ◽

The Impact ◽

Range Forecasts

AbstractThis study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.

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Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

Weather and Forecasting ◽

10.1175/waf-d-17-0010.1 ◽

2017 ◽

Vol 32 (5) ◽

pp. 1819-1840 ◽

Cited By ~ 48

Author(s):

David John Gagne ◽

Amy McGovern ◽

Sue Ellen Haupt ◽

Ryan A. Sobash ◽

John K. Williams ◽

...

Keyword(s):

Machine Learning ◽

Size Distribution ◽

Prediction Models ◽

Weather Prediction ◽

Radar Data ◽

Object Identification ◽

Atmospheric Conditions ◽

Learning Models ◽

Probabilistic Machine Learning ◽

Machine Learning Models

Abstract Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

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Initial Implementation of the Great Lakes Forecasting System: A Real-Time System for Predicting Lake Circulation and Thermal Structure

Water Quality Research Journal ◽

10.2166/wqrj.1994.014 ◽

1994 ◽

Vol 29 (2-3) ◽

pp. 203-220 ◽

Cited By ~ 52

Author(s):

D.J. Schwab ◽

K.W. Bedford

Keyword(s):

Great Lakes ◽

Real Time ◽

Prediction Models ◽

Thermal Structure ◽

Weather Prediction ◽

Model Output ◽

Real Time System ◽

Initial Implementation ◽

Lake Circulation ◽

Forecasting System

Abstract The Great Lakes Forecasting System is a real-time coastal prediction system for forecasting, on a daily basis, the physical state of each of the Great Lakes for the next two days. Forecast variables include the surface water level fluctuation, horizontal and vertical structure of temperature and currents, and turbulence. The system uses meteorological observations, satellite data, and forecasts from numerical weather prediction models as input. Lake circulation and thermal structure are calculated using a three-dimensional hydrodynamic prediction model. Output from the model is used to provide information on the current state of the lake and to predict changes for the next two days. This information is used by scientists, government agencies, commercial operations, and the public for enhancement of commercial and recreational activity, resource management, and hazard avoidance. This paper describes system design, data acquisition and analysis procedures, the hydrodynamic model, and sample model output. The initial implementation of the system provides daily nowcasts of system variables for one lake, Lake Erie. Requirements for implementing actual lake forecasts are discussed.

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