scholarly journals Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

2020 ◽  
Vol 148 (11) ◽  
pp. 4357-4375
Author(s):  
Josef Schröttle ◽  
Martin Weissmann ◽  
Leonhard Scheck ◽  
Axel Hutt
Keyword(s):  
Lead Time ◽  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
High Temporal Resolution ◽  
Convective Scale ◽  
Satellite Sensors ◽  
Satellite Channels ◽  
Infrared Radiances ◽  
Data Assimilation System

AbstractCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.

scholarly journals Development of Enhanced Vortex-Scale Atmospheric Motion Vectors for Hurricane Applications

2019 ◽  
Vol 11 (17) ◽  
pp. 1981 ◽  
Author(s):  
David Stettner ◽  
Christopher Velden ◽  
Robert Rabin ◽  
Steve Wanzong ◽  
Jaime Daniels ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Global Scale ◽  
Motion Vectors ◽  
Weather Events ◽  
Convective Scale ◽  
Satellite Sensors ◽  
Atmospheric Motion ◽  
Scanning Strategies ◽  
New Generation ◽  
Atmospheric Motion Vectors

Atmospheric motion vectors (AMVs) derived from geostationary meteorological satellites have long stood as an important observational contributor to analyses of global-scale tropospheric wind patterns. This paradigm is evolving as numerical weather prediction (NWP) models and associated data assimilation systems are at the point of trying to better resolve finer scales. Understanding the physical processes that govern convectively-driven weather systems is usually hindered by a lack of observations on the scales necessary to adequately describe these events. Fortunately, satellite sensors and associated scanning strategies have improved and are now able to resolve convective-scale flow fields. Coupled with the increased availability of computing capacity and more sophisticated algorithms to track cloud motions, we are now poised to investigate the development and application of AMVs to convective-scale weather events. Our study explores this frontier using new-generation GOES-R Series imagery with a focus on hurricane applications. A proposed procedure for processing enhanced AMV datasets derived from multispectral geostationary satellite imagery for hurricane-scale analyses is described. We focus on the use of the recently available GOES-16 mesoscale domain sector rapid-scan (1-min) imagery, and emerging methods to optimally extract wind estimates (atmospheric motion vectors (AMVs)) from close-in-time sequences. It is shown that AMV datasets can be generated on spatiotemporal scales not only useful for global applications, but for mesoscale applications such as hurricanes as well.

Temporal Changes in Wind as Objects for Evaluating Mesoscale Numerical Weather Prediction

2009 ◽  
Vol 24 (5) ◽  
pp. 1374-1389 ◽  
Author(s):  
Daran L. Rife ◽  
Christopher A. Davis ◽  
Jason C. Knievel
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Grid Point ◽  
Surface Wind ◽  
Temporal Changes ◽  
High Temporal Resolution ◽  
Numerical Weather ◽  
Wind Events

Abstract The study describes a method of evaluating numerical weather prediction models by comparing the characteristics of temporal changes in simulated and observed 10-m (AGL) winds. The method is demonstrated on a 1-yr collection of 1-day simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) over southern New Mexico. Temporal objects, or wind events, are defined at the observation locations and at each grid point in the model domain as vector wind changes over 2 h. Changes above the uppermost quartile of the distributions in the observations and simulations are empirically classified as significant; their attributes are analyzed and interpreted. It is demonstrated that the model can discriminate between large and modest wind changes on a pointwise basis, suggesting that many forecast events have an observational counterpart. Spatial clusters of significant wind events are highly continuous in space and time. Such continuity suggests that displaying maps of surface wind changes with high temporal resolution can alert forecasters to the occurrence of important phenomena. Documented systematic errors in the amplitude, direction, and timing of wind events will allow forecasters to mentally adjust for biases in features forecast by the model.

scholarly journals SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

2020 ◽  
Vol 146 (729) ◽  
pp. 1923-1938 ◽  
Author(s):  
B. C. Peter Heng ◽  
Robert Tubbs ◽  
Xiang‐Yu Huang ◽  
Bruce Macpherson ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

On Space Weather Data Assimilation

2020 ◽  
Author(s):  
Mihail Codrescu ◽  
Stefan Codrescu ◽  
Mariangel Fedrizzi ◽  
Claudia Borries
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Prediction Models ◽  
Mass Density ◽  
Weather Prediction ◽  
Weather Data ◽  
Space Systems ◽  
Use Of Data ◽  
Data Assimilation System ◽  
Numerical Weather Prediction Models

<p>Most if not all terrestrial weather prediction services today are based on data assimilation and numerical weather prediction models. Space Weather services are expected to follow a similar path towards data assimilation. However, the application of data assimilation in Space Weather requires a different implementation compared to terrestrial weather because space systems tend to be strongly forced and because the amount of data available for assimilation is critically small. In this paper we review the implementation of an ensemble Kalman filter data assimilation system based on the Space Weather Prediction Center operational Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model. We present assimilation results for neutral mass density during geomagnetically quiet and disturbed conditions and discuss the future use of data assimilation for the thermosphere ionosphere system.<span> </span></p>

scholarly journals The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 3: Meteorological evaluation of the on-line coupled system

2012 ◽  
Vol 5 (1) ◽  
pp. 129-147 ◽  
Author(s):  
C. Hofmann ◽  
A. Kerkweg ◽  
H. Wernli ◽  
P. Jöckel
Keyword(s):  
Atmospheric Chemistry ◽  
Lead Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Model ◽  
Coupled System ◽  
Regional Model ◽  
Model System ◽  
Convective Precipitation ◽  
On Line

Abstract. Three detailed meteorological case studies are conducted with the global and regional atmospheric chemistry model system ECHAM5/MESSy(→COSMO/MESSy)n, shortly named MECO(n). The aim of this article is to assess the general performance of the on-line coupling of the regional model COSMO to the global model ECHAM5. The cases are characterised by intense weather systems in Central Europe: a cold front passage in March 2010, a convective frontal event in July 2007, and the high impact winter storm "Kyrill" in January 2007. Simulations are performed with the new on-line-coupled model system and compared to classical, off-line COSMO hindcast simulations driven by ECMWF analyses. Precipitation observations from rain gauges and ECMWF analysis fields are used as reference, and both qualitative and quantitative measures are used to characterise the quality of the various simulations. It is shown that, not surprisingly, simulations with a shorter lead time generally produce more accurate simulations. Irrespective of lead time, the accuracy of the on-line and off-line COSMO simulations are comparable for the three cases. This result indicates that the new global and regional model system MECO(n) is able to simulate key mid-latitude weather systems, including cyclones, fronts, and convective precipitation, as accurately as present-day state-of-the-art regional weather prediction models in standard off-line configuration. Therefore, MECO(n) will be applied to simulate atmospheric chemistry exploring the model's full capabilities during meteorologically challenging conditions.

Evaluation of Forecasts of the Water Vapor Signature of Atmospheric Rivers in Operational Numerical Weather Prediction Models

2013 ◽  
Vol 28 (6) ◽  
pp. 1337-1352 ◽  
Author(s):  
Gary A. Wick ◽  
Paul J. Neiman ◽  
F. Martin Ralph ◽  
Thomas M. Hamill
Keyword(s):  
Water Vapor ◽  
Lead Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Time Model ◽  
Atmospheric Rivers ◽  
Position Bias ◽  
Forecast Lead Time ◽  
Northeastern Pacific ◽  
Numerical Weather Prediction Models

Abstract The ability of five operational ensemble forecast systems to accurately represent and predict atmospheric rivers (ARs) is evaluated as a function of lead time out to 10 days over the northeastern Pacific Ocean and west coast of North America. The study employs the recently developed Atmospheric River Detection Tool to compare the distinctive signature of ARs in integrated water vapor (IWV) fields from model forecasts and corresponding satellite-derived observations. The model forecast characteristics evaluated include the prediction of occurrence of ARs, the width of the IWV signature of ARs, their core strength as represented by the IWV content along the AR axis, and the occurrence and location of AR landfall. Analysis of three cool seasons shows that while the overall occurrence of ARs is well forecast out to a 10-day lead, forecasts of landfall occurrence are poorer, and skill degrades with increasing lead time. Average errors in the position of landfall are significant, increasing to over 800 km at 10-day lead time. Also, there is a 1°–2° southward position bias at 7-day lead time. The forecast IWV content along the AR axis possesses a slight moist bias averaged over the entire AR but little bias near landfall. The IWV biases are nearly independent of forecast lead time. Model spatial resolution is a factor in forecast skill and model differences are greatest for forecasts of AR width. This width error is greatest for coarser-resolution models that have positive width biases that increase with forecast lead time.

FROST-2014: The Sochi Winter Olympics International Project

2017 ◽  
Vol 98 (9) ◽  
pp. 1908-1929 ◽  
Author(s):  
Dmitry Kiktev ◽  
Paul Joe ◽  
George A. Isaac ◽  
Andrea Montani ◽  
Inger-Lise Frogner ◽  
...  
Keyword(s):  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Winter Olympics ◽  
Systems Model ◽  
Weather Observations ◽  
Prediction Systems ◽  
Development And Validation

Abstract The World Meteorological Organization (WMO) World Weather Research Programme’s (WWRP) Forecast and Research in the Olympic Sochi Testbed program (FROST-2014) was aimed at the advancement and demonstration of state-of-the-art nowcasting and short-range forecasting systems for winter conditions in mountainous terrain. The project field campaign was held during the 2014 XXII Olympic and XI Paralympic Winter Games and preceding test events in Sochi, Russia. An enhanced network of in situ and remote sensing observations supported weather predictions and their verification. Six nowcasting systems (model based, radar tracking, and combined nowcasting systems), nine deterministic mesoscale numerical weather prediction models (with grid spacings down to 250 m), and six ensemble prediction systems (including two with explicitly simulated deep convection) participated in FROST-2014. The project provided forecast input for the meteorological support of the Sochi Olympic Games. The FROST-2014 archive of winter weather observations and forecasts is a valuable information resource for mesoscale predictability studies as well as for the development and validation of nowcasting and forecasting systems in complex terrain. The resulting innovative technologies, exchange of experience, and professional developments contributed to the success of the Olympics and left a post-Olympic legacy.

scholarly journals Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME

2012 ◽  
Vol 51 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Lisa Bengtsson ◽  
Sander Tijm ◽  
Filip Váňa ◽  
Gunilla Svensson
Keyword(s):  
Prediction Models ◽  
Mesoscale Model ◽  
Deep Convection ◽  
Weather Prediction ◽  
Nonlinear Flow ◽  
Dynamical Model ◽  
Convective Precipitation ◽  
Physical Parameters ◽  
Rain Intensity ◽  
Horizontal Diffusion

AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

scholarly journals Predicting Small-Scale, Short-Lived Downbursts: Case Study with the NWP Limited-Area ALARO Model for the Pukkelpop Thunderstorm

2015 ◽  
Vol 143 (3) ◽  
pp. 742-756 ◽  
Author(s):  
Pieter De Meutter ◽  
Luc Gerard ◽  
Geert Smet ◽  
Karim Hamid ◽  
Rafiq Hamdi ◽  
...  
Keyword(s):  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
Parameterization Scheme ◽  
Small Scale ◽  
Limited Area ◽  
Operational Model ◽  
Music Festival ◽  
Forecast Models

Abstract The authors consider a thunderstorm event in 2011 during a music festival in Belgium that produced a short-lived downburst of a diameter of less than 100 m. This is far too small to be resolved by the kilometric resolutions of today’s operational numerical weather prediction models. Operational forecast models will not run at hectometric resolutions in the foreseeable future. The storm caused five casualties and raised strong societal questions regarding the predictability of such a traumatic weather event. In this paper it is investigated whether the downdrafts of a parameterization scheme of deep convection can be used as proxies for the unresolved downbursts. To this end the operational model ALARO [a version of the Action de Recherche Petite Echelle Grande Echelle-Aire Limitée Adaptation Dynamique Développement International (ARPEGE-ALADIN) operational limited area model with a revised and modular structure of the physical parameterizations] of the Royal Meteorological Institute of Belgium is used. While the model in its operational configuration at the time of the event did not give a clear hint of a downburst event, it has been found that (i) the use of unsaturated downdrafts and (ii) some adaptations of the features of this downdraft parameterization scheme, specifically the sensitivity to the entrainment and friction, can make the downdrafts sensitive enough to the surrounding resolved-scale conditions to make them useful as indicators of the possibility of such downbursts.

scholarly journals Improving Harvey Forecasts with Next-Generation Weather Satellites: Advanced Hurricane Analysis and Prediction with Assimilation of GOES-R All-Sky Radiances

2019 ◽  
Vol 100 (7) ◽  
pp. 1217-1222 ◽  
Author(s):  
Fuqing Zhang ◽  
Masashi Minamide ◽  
Robert G. Nystrom ◽  
Xingchao Chen ◽  
Shian-Jian Lin ◽  
...  
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Gulf Coast ◽  
Weather Prediction ◽  
Next Generation ◽  
Emergency Managers ◽  
Advanced Analysis ◽  
Potential Improvement ◽  
Infrared Radiances ◽  
Gulf Coast Region

AbstractHurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.

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