Lower-Tropospheric Enhancement of Gravity Wave Drag in a Global Spectral Atmospheric Forecast Model

2008 ◽  
Vol 23 (3) ◽  
pp. 523-531 ◽  
Author(s):  
Song-You Hong ◽  
Jung Choi ◽  
Eun-Chul Chang ◽  
Hoon Park ◽  
Young-Joon Kim
Keyword(s):  
Gravity Wave ◽  
Large Scale ◽  
Weather Prediction ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
Wave Drag ◽  
Pressure System ◽  
Global Spectral Model ◽  
Medium Range ◽  
Range Forecasts

Abstract The impacts of enhanced lower-tropospheric gravity wave drag induced by subgrid-scale orography on short- and medium-range forecasts as well as seasonal simulations are examined. This study reports on the enhanced performance of the scheme proposed by Kim and Arakawa, which has been used in the National Centers for Environmental Prediction (NCEP) Global Spectral Model since 1997. The performance is evaluated against a traditional upper-level drag scheme that is also available in the model. The experiment results reveal that the Kim–Arakawa scheme improves the movement and intensity of an extratropical cyclone and a continental high pressure system that was accompanied by heavy snowfall over Korea on 14–15 February 2001. The monthly verification for medium-range forecasts in December 2006, which are initialized by the NCEP operational analysis, demonstrates overall improvements in the forecasts of large-scale fields in the Northern Hemisphere. Moderate improvements are also found in the seasonal simulation of December–February for the years 1996/97, 1997/98, and 1999/2000. This study concludes that the enhanced lower-level drag should be properly parameterized in global atmospheric models for numerical weather prediction and seasonal prediction.

Effects of Non-orographic Gravity Wave Drag on Seasonal and Medium-range Predictions in a Global Forecast Model

2018 ◽  
Vol 54 (S1) ◽  
pp. 385-402 ◽  
Author(s):  
Hyun-Joo Choi ◽  
Ji-Young Han ◽  
Myung-Seo Koo ◽  
Hye-Yeong Chun ◽  
Young-Ha Kim ◽  
...  
Keyword(s):  
Gravity Wave ◽  
Forecast Model ◽  
Wave Drag ◽  
Medium Range

Influence of gravity wave drag parametrisations on the stratospheric circulation of seasonal ICON-NWP experiments

2020 ◽  
Author(s):  
Raphael Köhler ◽  
Dörthe Handorf ◽  
Ralf Jaiser ◽  
Klaus Dethloff ◽  
Günther Zängl ◽  
...  
Keyword(s):  
Gravity Wave ◽  
General Circulation ◽  
Weather Prediction ◽  
Polar Vortex ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Wave Drag ◽  
Stratospheric Polar Vortex ◽  
Sensitivity Experiments ◽  
Hemisphere Winter

<p>The stratospheric polar vortex is highly variable in winter and thus, models often struggle to capture its variability and strength. Yet, the influence of the stratosphere on the tropospheric circulation becomes highly important in Northern Hemisphere winter and is one of the main potential sources for subseasonal to seasonal prediction skill in mid latitudes. Mid-latitude extreme weather patterns in winter are often preceded by sudden stratospheric warmings (SSWs), which are the strongest manifestation of the coupling between stratosphere and troposphere. Misrepresentation of the SSW-frequency and stratospheric biases in models can therefore also cause biases in the troposphere.</p><p>In this context this work comprises the analysis of four seasonal ensemble experiments with a high-resolution, nonhydrostatic global atmospheric general circulation model in numerical weather prediction mode (ICON-NWP). The main focus thereby lies on the variability and strength of the stratospheric polar vortex. We identified the gravity wave drag parametrisations as one important factor influencing stratospheric dynamics. As the control experiment with default gravity wave drag settings exhibits an overestimated amount of SSWs and a weak stratospheric polar vortex, three sensitivity experiments with adjusted drag parametrisations were generated. Hence, the parametrisations for the non-orographic gravity wave drag and the subgrid‐scale orographic (SSO) drag were chosen with the goal of strengthening the stratospheric polar vortex. Biases to ERA-Interim are reduced with both adjustments, especially in high latitudes. Whereas the positive effect of the reduced non-orographic gravity wave drag is strongest in the mid-stratosphere in winter, the adjusted SSO-scheme primarily affects the troposphere by reducing mean sea level pressure biases in all months. A fourth experiment using both adjustments exhibits improvements in the troposphere and stratosphere. Although the stratospheric polar vortex in winter is strengthened in all sensitivity experiments, it is still simulated too weak compared to ERA-Interim. Further mechanisms causing this weakness are also investigated in this study.</p>

Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

Improving Numerical Weather Prediction-Based Near-Cloud Aviation Turbulence Forecasts by Diagnosing Convective Gravity Wave Breaking

2021 ◽  
Author(s):  
Soo-Hyun Kim ◽  
Hye-Yeong Chun ◽  
Dan-Bi Lee ◽  
Jung-Hoon Kim ◽  
Robert D. Sharman
Keyword(s):  
Gravity Wave ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Area Under The Curve ◽  
Wave Drag ◽  
Probability Of Detection ◽  
Numerical Weather ◽  
Air Turbulence ◽  
One Year ◽  
Nwp Model

AbstractBased on a convective gravity wave drag parameterization scheme in a Numerical Weather Prediction (NWP) model, previously proposed near-cloud turbulence (NCT) diagnostics for better detecting turbulence near convection are tested and evaluated by using global in situ flight data and outputs from operational global NWP model of the Korea Meteorological Administration for one year (from December 2016 to November 2017). For comparison, eleven widely used clear air turbulence (CAT) diagnostics currently used in operational NWP-based aviation turbulence forecasting systems are separately computed. For selected cases, NCT diagnostics predict more accurately localized turbulence events over convective regions with better intensity, which is clearly distinguished from the turbulence areas diagnosed by conventional CAT diagnostics that they mostly failed to forecast with broad areas and low magnitudes. Although overall performance of NCT diagnostics for whole one year is lower than conventional CAT diagnostics due to the fact that NCT diagnostics exclusively focus on the isolated NCT events, adding the NCT diagnostics to CAT diagnostics improves the performance of aviation turbulence forecasting. Especially in the summertime, performance in terms of an area under the curve (AUC) based on probability of detection statistics is the best (AUC = 0.837 with a 4% increase, compared to conventional CAT forecasts) when the mean of all CAT and NCT diagnostics is used, while performance in terms of root mean square error is the best when the maximum among combined CAT and single NCT diagnostic is used. This implies that including NCT diagnostics to currently used NWP-based aviation turbulence forecasting systems should be beneficial for safety of air travel.

Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California

2020 ◽  
Vol 148 (8) ◽  
pp. 3489-3506
Author(s):  
Michael Scheuerer ◽  
Matthew B. Switanek ◽  
Rochelle P. Worsnop ◽  
Thomas M. Hamill
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.

scholarly journals Prediction and Diagnosis of Tropical Cyclone Formation in an NWP System. Part III: Diagnosis of Developing and Nondeveloping Storms

2007 ◽  
Vol 64 (9) ◽  
pp. 3195-3213 ◽  
Author(s):  
K. J. Tory ◽  
N. E. Davidson ◽  
M. T. Montgomery
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Deep Convection ◽  
Weather Prediction ◽  
Vertical Wind Shear ◽  
Forecast Model ◽  
Vertical Shear ◽  
Limited Area ◽  
Vortex Interactions ◽  
Secondary Mechanism

Abstract This is the third of a three-part investigation into tropical cyclone (TC) genesis in the Australian Bureau of Meteorology’s Tropical Cyclone Limited Area Prediction System (TC-LAPS), an operational numerical weather prediction (NWP) forecast model. In Parts I and II, a primary and two secondary vortex enhancement mechanisms were illustrated, and shown to be responsible for TC genesis in a simulation of TC Chris. In this paper, five more TC-LAPS simulations are investigated: three developing and two nondeveloping. In each developing simulation the pathway to genesis was essentially the same as that reported in Part II. Potential vorticity (PV) cores developed through low- to middle-tropospheric vortex enhancement in model-resolved updraft cores (primary mechanism) and interacted to form larger cores through diabatic upscale vortex cascade (secondary mechanism). On the system scale, vortex intensification resulted from the large-scale mass redistribution forced by the upward mass flux, driven by diabatic heating, in the updraft cores (secondary mechanism). The nondeveloping cases illustrated that genesis can be hampered by (i) vertical wind shear, which may tilt and tear apart the PV cores as they develop, and (ii) an insufficient large-scale cyclonic environment, which may fail to sufficiently confine the warming and enhanced cyclonic winds, associated with the atmospheric adjustment to the convective updrafts. The exact detail of the vortex interactions was found to be unimportant for qualitative genesis forecast success. Instead the critical ingredients were found to be sufficient net deep convection in a sufficiently cyclonic environment in which vertical shear was less than some destructive limit. The often-observed TC genesis pattern of convection convergence, where the active convective regions converge into a 100-km-diameter center, prior to an intense convective burst and development to tropical storm intensity is evident in the developing TC-LAPS simulations. The simulations presented in this study and numerous other simulations not yet reported on have shown good qualitative forecast success. Assuming such success continues in a more rigorous study (currently under way) it could be argued that TC genesis is largely predictable provided the large-scale environment (vorticity, vertical shear, and convective forcing) is sufficiently resolved and initialized.

scholarly journals Representation of the 2016 Korean Heatwave in the Unified Model Global NWP Forecasts: The Impact of Remotely Forced Model Errors and Atmosphere-Ocean Coupling

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1275
Author(s):  
Eun-Jung Kim ◽  
Charline Marzin ◽  
Sean F. Milton ◽  
Kyung-On Boo ◽  
Yoonjae Kim ◽  
...  
Keyword(s):  
Heat Wave ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Unified Model ◽  
Northwest Pacific ◽  
Model Errors ◽  
Pressure System ◽  
Ocean Coupling ◽  
The Impact

This study investigates the effects of atmosphere-ocean coupling for medium-range forecasts by using coupled numerical weather prediction (NWP) experiments based on the unified model (UM) on a case study of the 2016 heatwave over the Korean Peninsula. Atmospheric nudging experiments were carried out to determine the key regions which may have large impacts on the forecasts of the heat wave. The results of the nudging experiments suggest that key forcing from the Mongolia region gives the largest impact to this case by causing a transport of warm air from the northwest part of Korea. Moreover, the Pacific region shows an important role in the global circulation in nudging experiments. Results from the atmosphere-ocean coupled model show no clear benefit for the extreme heat wave temperatures in this case. In addition, more model development seems to be needed to improve the representation of sea surface temperature (SST) in some key areas. Nevertheless, it is confirmed that the atmosphere-ocean coupled simulation produces a better representation of aspects of the large-scale flow such as the blocking high over the Kamchatka Peninsula, the high pressure system in the northwest Pacific and Hadley circulation. The results presented in this study show that atmosphere-ocean coupling can be an important way to improve the deterministic model forecasts as the lead time increases beyond a few days.

Sign in / Sign up

Export Citation Format

Share Document