scholarly journals An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting

2006 ◽  
Vol 21 (3) ◽  
pp. 268-287 ◽  
Author(s):  
R. Sharman ◽  
C. Tebaldi ◽  
G. Wiener ◽  
J. Wolff
Keyword(s):  
Weather Prediction ◽  
Integrated Approach ◽  
Numerical Weather Prediction Model ◽  
Guidance System ◽  
Forecast Errors ◽  
Weighted Sum ◽  
Statistical Verification ◽  
Diagnostic Threshold ◽  
Overall Performance ◽  
Upper Level

Abstract An automated procedure for forecasting mid- and upper-level turbulence that affects aircraft is described. This procedure, termed the Graphical Turbulence Guidance system, uses output from numerical weather prediction model forecasts to derive many turbulence diagnostics that are combined as a weighted sum with the relative weights computed to give best agreement with the most recent available turbulence observations (i.e., pilot reports of turbulence or PIREPs). This procedure minimizes forecast errors due to uncertainties in individual turbulence diagnostics and their thresholds. Thorough statistical verification studies have been performed that focused on the probabilities of correct detections of yes and no PIREPs by the forecast algorithm. Using these statistics as a guide, the authors have been able to intercompare individual diagnostic performance, and test various diagnostic threshold and weighting strategies. The overall performance of the turbulence forecast and the effect of these strategies on performance are described.

scholarly journals Diagnosis of Track Forecast Errors for Tropical Cyclone Rita (2005) Using GEFS Reforecasts

2015 ◽  
Vol 30 (5) ◽  
pp. 1334-1354 ◽  
Author(s):  
Thomas J. Galarneau ◽  
Thomas M. Hamill
Keyword(s):  
Tropical Cyclone ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Track Forecast ◽  
Forecast Errors ◽  
Subtropical Anticyclone ◽  
Cutoff Low ◽  
Layer Flow ◽  
Upper Level ◽  
Mass Evacuation

Abstract Analysis and diagnosis of the track forecasts for Tropical Cyclone (TC) Rita (2005) from the Global Ensemble Forecast System (GEFS) reforecast dataset is presented. The operational numerical weather prediction guidance and GEFS reforecasts initialized at 0000 UTC 20–22 September 2005, 2–4 days prior to landfall, were all characterized by a persistent left-of-track error. The numerical guidance indicated a significant threat of landfall for the Houston, Texas, region on 24 September. The largest mass evacuation in U.S. history was ordered, with the evacuation resulting in more fatalities than TC Rita itself. TC Rita made landfall along the Texas–Louisiana coastal zone on 24 September. This study utilizes forecasts from the GEFS reforecast and a high-resolution regional reforecast. The regional reforecast was generated using the Advanced Hurricane Weather Research and Forecasting Model (AHW) with the GEFS reforecasts providing the initial and boundary conditions. The results show that TC Rita’s track was sensitive to errors in both the synoptic-scale flow and TC intensity. Within the GEFS reforecast ensemble, the nonrecurving members were characterized by a midlevel subtropical anticyclone that extended too far south and west over the southern United States, and an upper-level cutoff low west and anticyclone east of TC Rita that were too weak. The AHW regional reforecast ensemble further highlighted the role of intensity and steering-layer depth in TC Rita’s track. While the AHW forecast was initialized with a TC that was too weak, the ensemble members that were able to intensify TC Rita more rapidly produced a better track forecast because the TCs followed a deeper steering-layer flow.

scholarly journals Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales

2016 ◽  
Vol 144 (4) ◽  
pp. 1273-1298 ◽  
Author(s):  
Yunji Zhang ◽  
Fuqing Zhang ◽  
David J. Stensrud ◽  
Zhiyong Meng
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Lead Times ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Positive Feedbacks ◽  
Convective Storms ◽  
Cold Pools ◽  
Initial Errors ◽  
Environmental Instability

Abstract Using a high-resolution convection-allowing numerical weather prediction model, this study seeks to explore the intrinsic predictability of the severe tornadic thunderstorm event on 20 May 2013 in Oklahoma from its preinitiation environment to initiation, upscale organization, and interaction with other convective storms. This is accomplished through ensemble forecasts perturbed with minute initial condition uncertainties that were beyond detection capabilities of any current observational platforms. It was found that these small perturbations, too small to modify the initial mesoscale environmental instability and moisture fields, will be propagated and evolved via turbulence within the PBL and rapidly amplified in moist convective processes through positive feedbacks associated with updrafts, phase transitions of water species, and cold pools, thus greatly affecting the appearance, organization, and development of thunderstorms. The forecast errors remain nearly unchanged even when the initial perturbations (errors) were reduced by as much as 90%, which strongly suggests an inherently limited predictability for this thunderstorm event for lead times as short as 3–6 h. Further scale decomposition reveals rapid error growth and saturation in meso-γ scales (regardless of the magnitude of initial errors) and subsequent upscale growth into meso-β scales.

scholarly journals Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011

2012 ◽  
Vol 140 (8) ◽  
pp. 2609-2627 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Edward R. Mansell ◽  
Conrad L. Ziegler ◽  
Donald R. MacGorman
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Numerical Weather Prediction Model ◽  
Analysis Time ◽  
Test Case ◽  
Forecast Errors ◽  
Mixing Ratio ◽  
Total Lightning ◽  
Tornado Outbreak

Abstract This study presents the assimilation of total lightning data to help initiate convection at cloud-resolving scales within a numerical weather prediction model. The test case is the 24 May 2011 Oklahoma tornado outbreak, which was characterized by an exceptional synoptic/mesoscale setup for the development of long-lived supercells with large destructive tornadoes. In an attempt to reproduce the observed storms at a predetermined analysis time, total lightning data were assimilated into the Weather Research and Forecasting Model (WRF) and analyzed via a suite of simple numerical experiments. Lightning data assimilation forced deep, moist precipitating convection to occur in the model at roughly the locations and intensities of the observed storms as depicted by observations from the National Severe Storms Laboratory’s three-dimensional National Mosaic and Multisensor Quantitative Precipitation Estimation (QPE)—i.e., NMQ—radar reflectivity mosaic product. The nudging function for the total lightning data locally increases the water vapor mixing ratio (and hence relative humidity) via a simple smooth continuous function using gridded pseudo-Geostationary Lightning Mapper (GLM) resolution (9 km) flash rate and simulated graupel mixing ratio as input variables. The assimilation of the total lightning data for only a few hours prior to the analysis time significantly improved the representation of the convection at analysis time and at the 1-h forecast within the convective permitting and convective resolving grids (i.e., 3 and 1 km, respectively). The results also highlighted possible forecast errors resulting from errors in the initial mesoscale thermodynamic variable fields. Although this case was primarily an analysis rather than a forecast, this simple and computationally inexpensive assimilation technique showed promising results and could be useful when applied to events characterized by moderate to intense lightning activity.

On the Assessment of a Numerical Weather Prediction Model for Solar Photovoltaic Power Forecasts in Cities

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Harold Gamarro ◽  
Jorge E. Gonzalez ◽  
Luis E. Ortiz
Keyword(s):  
New York ◽  
Weather Prediction ◽  
Wrf Model ◽  
Urban Canopy ◽  
Region Of Interest ◽  
Integrated Approach ◽  
Numerical Weather Prediction Model ◽  
Recent Developments ◽  
Sky Conditions ◽  
The City

Recent developments in the weather research and forecasting (WRF) model have made it possible to accurately estimate incident solar radiation. This study couples the WRF-solar modifications with a multilayer urban canopy and building energy model (BEM) to create a unified WRF forecasting system called urban WRF–solar (uWRF-solar). This paper tests the integrated approach in the New York City (NYC) metro region as a sample case. Hourly forecasts are validated against ground station data collected at ten different sites in and around the city. Validation is carried out independently for clear, cloudy, and overcast sky conditions. Results indicate that the uWRF-solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R2 value of 0.93 for clear sky conditions, 0.61 for cloudy sky conditions, and finally, 0.39 for overcast conditions. Results are further used to directly forecast solar power production in the region of interest, where evaluations of generation potential are done at the city scale. Outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-solar grid. In total, the city has a city photovoltaic (PV) potential of 118 kWh/day/m2 and 3.65 MWh/month/m2.

scholarly journals Atmospheric Sensitivity to SST near the Kuroshio Extension during the Extratropical Transition of Typhoon Tokage*

2010 ◽  
Vol 138 (7) ◽  
pp. 2644-2663 ◽  
Author(s):  
Nicholas A. Bond ◽  
Meghan F. Cronin ◽  
Matthew Garvert
Keyword(s):  
North Pacific ◽  
Kuroshio Extension ◽  
Weather Prediction ◽  
Maximum Amplitude ◽  
Numerical Weather Prediction Model ◽  
Heat Fluxes ◽  
Extratropical Transition ◽  
The North ◽  
Upper Level ◽  
The Kuroshio

Abstract It is hypothesized that the tropical-to-extratropical transition of a cyclone in the western North Pacific can be sensitive to the underlying sea surface temperature (SST) distribution. This hypothesis was tested through a case study of Typhoon Tokage using a series of high-resolution simulations by the Weather Research Forecast (WRF) numerical weather prediction model. Simulations were carried out for a control SST distribution and for SST distributions with imposed warm and cold perturbations of 1.5°C maximum amplitude in the vicinity of the Kuroshio Extension. The simulations with the warm SST perturbation yielded a cyclone slightly weaker than in the control SST case about 2 days after transition. In contrast, the cold SST perturbation case yielded a cyclone with a central pressure 10 hPa lower than in the control case at the same point in the storm’s life cycle, apparently due to its more northward track and hence closer proximity to an approaching upper-level trough and perhaps in association with a stronger warm front. The effects of the regional SST on the simulated storms are manifested not just locally, but also cause substantial impacts on 500-hPa geopotential heights over much of the North Pacific basin. Retrospective analysis of meridional heat fluxes associated with these events using the NCEP–NCAR reanalysis was carried out for early fall (September–November) seasons with relatively warm and cool SST in the region of the imposed SST perturbations. Differences in the patterns of these fluxes between the warm and cool years are broadly consistent with the results from the warm versus cool SST simulations for Typhoon Tokage.

scholarly journals Improving Precipitation Forecasts by Generating Ensembles through Postprocessing

2015 ◽  
Vol 143 (9) ◽  
pp. 3642-3663 ◽  
Author(s):  
Durga Lal Shrestha ◽  
David E. Robertson ◽  
James C. Bennett ◽  
Q. J. Wang
Keyword(s):  
Weather Prediction ◽  
Joint Probability ◽  
Numerical Weather Prediction Model ◽  
Climatic Conditions ◽  
Lead Times ◽  
Forecast Errors ◽  
Streamflow Forecasting ◽  
Ensemble Forecasts ◽  
Modeling Approach ◽  
Wide Range

Abstract This paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach. Calibrated QPFs are produced for 10 Australian catchments that cover a wide range of climatic conditions and hydrological characteristics. The calibrated QPFs are bias free, contain smaller forecast errors than that of the raw QPFs, reliably quantify the forecast uncertainty at a range of lead times, and successfully discriminate common and rare events of precipitation occurrences at shorter lead times. The postprocessing method is able to instill realistic within-catchment spatial variability in the QPFs, which is crucial for accurate and reliable streamflow forecasting.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

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