Browse

You are looking at 1 - 10 of 2,821 items for :

  • Weather and Forecasting x
  • Refine by Access: Content accessible to me x
Clear All
Samuel R. Harrison, James O. Pope, Robert A. Neal, Freya K. Garry, Ryosuke Kurashina, and Dan Suri

Abstract

Icelandic volcanic emissions have been shown historically and more recently to have an impact on public health and aviation across northern and western Europe. The severity of these impacts is governed by the prevailing weather conditions and the nature of the eruption. This study focuses on the former utilizing an existing set of 30 weather patterns produced by the Met Office. Associated daily historical classifications are used to assess which weather patterns are most likely to result in flow from Iceland into four flight information regions (FIRs) covering the British Isles and North Atlantic, which may lead to disruption to aviation during Icelandic volcanic episodes. High-risk weather patterns vary between FIRs, with a total of 14 weather patterns impacting at least one FIR. These high-risk types predominantly have a northwesterly or westerly flow from Iceland into British Isles airspace. Analysis of the historical classifications reveals a typical duration for high-risk periods of 3–5 days, when transitions between high-risk types are considered. High-risk periods lasting over a week are also possible in all four FIRs. Additionally, impacts are more likely in winter months for most FIRs. Knowledge of high-risk weather patterns for aviation can be used within existing operational probabilistic weather pattern forecasting tools. Combined probabilities for high-risk weather patterns can be derived for the medium-range (1–2 weeks ahead) and used to provide a rapid assessment as to the likelihood of flow from Iceland. This weather pattern forecasting application is illustrated using archived forecast data for the 2010 Eyjafjallajökull eruption.

Open access
M. V. Bilskie, T. G. Asher, P. W. Miller, J. G. Fleming, S. C. Hagen, and R. A. Luettich Jr.

Abstract

Storm surge caused by tropical cyclones can cause overland flooding and lead to loss of life while damaging homes, businesses, and critical infrastructure. In 2018, Hurricane Michael made landfall near Mexico Beach, Florida, on 10 October with peak wind speeds near 71.9 m s−1 (161 mph) and storm surge over 4.5 m NAVD88. During Hurricane Michael, water levels and waves were predicted near–real time using a deterministic, depth-averaged, high-resolution ADCIRC+SWAN model of the northern Gulf of Mexico. The model was forced with an asymmetrical parametric vortex model [generalized asymmetric Holland model (GAHM)] based on Michael’s National Hurricane Center (NHC) forecast track and strength. The authors report errors between simulated and observed water level time series, peak water level, and timing of peak for NHC advisories. Forecasts of water levels were within 0.5 m of observations, and the timing of peak water levels was within 1 h as early as 48 h before Michael’s eventual landfall. We also examined the effect of adding far-field meteorology in our TC vortex model for use in real-time forecasts. In general, we found that including far-field meteorology by blending the TC vortex with a basin-scale NWP product improved water level forecasts. However, we note that divergence between the NHC forecast track and the forecast track of the meteorological model supplying the far-field winds represents a potential limitation to operationalizing a blended wind field surge product. The approaches and data reported herein provide a transparent assessment of water level forecasts during Hurricane Michael and highlight potential future improvements for more accurate predictions.

Open access
Eric P. James, Curtis R. Alexander, David C. Dowell, Stephen S. Weygandt, Stanley G. Benjamin, Geoffrey S. Manikin, John M. Brown, Joseph B. Olson, Ming Hu, Tatiana G. Smirnova, Terra Ladwig, Jaymes S. Kenyon, and David D. Turner

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Weather Research and Forecast model (WRF-ARW) that covers the conterminous United States and Alaska and runs hourly (for CONUS; every three hours for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and non-conventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Open access
Stephen S. Weygandt, Stanley G. Benjamin, Ming Hu, Curtis R. Alexander, Tatiana G. Smirnova, and Eric P. James

Abstract

A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial-gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent-heating-based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the ‘Radar-LHI’ (latent-heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent-heat-derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0-6 hour) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR.

Open access
Zuohao Cao, Stéphane Bélair, and Da-Lin Zhang

Abstract

A short-range regional, two-way coupled atmosphere-ocean-ice model has been recently developed in an attempt to improve, among other things, quantitative precipitation forecasts (QPFs) over southern Ontario, Canada by incorporating air-lake interaction over the Great Lakes region. Here, we attempt to (1) assess the impact of the air-lake coupling on daily QPFs, as verified against the Canadian Precipitation Analysis and independent observations, over southern Ontario during the period of June 2016–May 2017; (2) diagnose major physical processes governing the QPF differences between the coupled and uncoupled models by relating precipitation to those processes at the air-water interface and above. Results indicate that the coupled model tends to reduce the area- and monthly-averaged daily QPF biases and standard deviations in 5 months of October, November, and December 2016, and April and May 2017, but increase and deteriorate precipitation biases during the summer months. Most of the deteriorations occur during the daytime, while improvements are observed during the nighttime (in 7 of 12 months). During the daytime, slight improvements appear in 2 months. A further diagnosis indicates that the daily QPF differences between the two models are highly correlated with the differences of their sensible and latent heat fluxes. The maximum (minimum) difference of sensible (latent) heat flux in August 2016 (December 2016) is in phase with the maximum (minimum) difference of the two-model daily QPFs. The daily QPF differences in the other months are also controlled by the differences of vertically integrated water vapor flux convergence, and surface temperature.

Open access
Benjamin J. E. Schroeter, Nathaniel L. Bindoff, Phil Reid, and Simon P. Alexander

Abstract

The special observing periods (SOPs) of the Year of Polar Prediction present an opportunity to assess the skill of numerical weather prediction (NWP) models operating over the Antarctic, many of which assimilated additional observations during an SOP to produce some of the most observationally informed model output to date for the Antarctic region and permitting closer examination of model performance under various configurations and parameterizations. This intercomparison evaluates six NWP models spanning global and limited domains, coupled and uncoupled, operating in the Antarctic during the austral summer SOP between 16 November 2018 and 15 February 2019. Model performance varies regionally between each model and parameter; however, the majority of models were found to be warm biased over the continent with respect to ERA5 at analysis, some with biases growing to 3.5 K over land after 48 h. Temperature biases over sea ice were found to be strongly correlated between analysis and 48 h in uncoupled models, but that this correlation can be reduced through coupling to a sea ice model. Surface pressure and 500-hPa geopotential height forecasts and biases were found to be strongly correlated over open ocean in all models, and wind speed forecasts were found to be generally more skillful at higher resolutions with the exception of fast modeled winds over sloping terrain in PolarWRF. Surface sensible and latent heat flux forecasts and biases produced diverse correlations, varying by model, parameter, and gridcell classification. Of the models evaluated, those which couple atmosphere, sea ice, and ocean typically exhibited stronger skill.

Significance Statement

We evaluated the performance of six numerical weather prediction models operating over the Antarctic during the Year of Polar Prediction austral summer special observing period (16 November 2018–15 February 2019). Our analysis found that several models were as much as 3.5 K warmer than the reference analysis (ERA5) at 48 h over land and were strongly correlated over sea ice in uncoupled models; however, this correlation is reduced through coupling to a sea ice model. Surface pressure biases are communicated to the midtroposphere over the ocean at larger spatial scales, while higher resolution showed an increase in positive wind biases at longer forecasts. Surface turbulent heat fluxes produced complex correlations with other forecast parameters, which should be quantified in future studies. Coupled models that included an ocean/sea ice component typically performed better; providing evidence that the inclusion of such components leads to improved model performance, even at short time scales such as these.

Open access
Free access
Sebastian Buschow

Abstract

When highly resolved precipitation forecasts are verified against observations, displacement errors tend to overshadow all other aspects of forecast quality. The appropriate treatment and explicit measurement of such errors remains a challenging task. This study explores a new verification technique that uses the phase of complex wavelet coefficients to quantify spatially varying displacements. Idealized and realistic test cases from the MesoVICT project demonstrate that our approach yields helpful results in a variety of situations where popular alternatives may struggle. Potential benefits of very high spatial resolutions can be identified even when the observational dataset is coarsely resolved itself. The new score can furthermore be applied not only to precipitation but also variables such as wind speed and potential temperature, thereby overcoming a limitation of many established location scores.

Significance Statement

One important requirement for a useful weather forecast is its ability to predict the placement of weather events such as cold fronts, low pressure systems, or groups of thunderstorms. Errors in the predicted location are not easy to quantify: some established quality measures combine location and other error sources in one score, others are only applicable if the data contain well-defined and easily identifiable objects. Here we introduce an alternative location score that avoids such assumptions and is thus widely applicable. As an additional benefit, we can separate displacement errors into different spatial scales and localize them on a weather map.

Open access
Jadwiga H. Richter, Anne A. Glanville, James Edwards, Brian Kauffman, Nicholas A. Davis, Abigail Jaye, Hyemi Kim, Nicholas M. Pedatella, Lantao Sun, Judith Berner, Who M. Kim, Stephen G. Yeager, Gokhan Danabasoglu, Julie M. Caron, and Keith W. Oleson

Abstract

Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.

Significance Statement

We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

Open access
Wei Sun, Zhiquan Liu, Guiting Song, Yangyang Zhao, Shan Guo, Feifei Shen, and Xiangming Sun

Abstract

To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).

Open access