scholarly journals Improving Week-2 Forecasts with Multimodel Reforecast Ensembles

2006 ◽  
Vol 134 (8) ◽  
pp. 2279-2284 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Xue Wei ◽  
Frédéric Vitart
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Model Output ◽  
Single Model ◽  
Global Models ◽  
Operational Forecast ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

2005 ◽  
Vol 20 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang
Keyword(s):  
The Netherlands ◽  
Important Predictor ◽  
Convective Precipitation ◽  
Square Root ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorm ◽  
Severe Thunderstorms ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract The derivation and verification of logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands is described. For 12 regions of about 90 km × 80 km each, and for projections out to 48 h in advance (with 6-h periods), these equations have been derived using model output statistics (MOS). As a source for the predictands, lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset mainly consisted of the combined (postprocessed) output from two numerical weather prediction (NWP) models. It contained 15 traditional thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (postprocessed) output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The most important predictor in the thunderstorm forecast system is the square root of the ECMWF 6-h convective precipitation sum, and the most important predictor in the severe thunderstorm forecast system is the HIRLAM Boyden index. The success of the square root of the ECMWF 6-h convective precipitation sum as a thunderstorm predictor indicates that there is a strong relation between the forecast convective precipitation by the ECMWF model and the occurrence of thunderstorms, at least in the Netherlands up to 3 days in advance. The overall verification results for the 0000, 0600, 1200, and 1800 UTC runs of the MOS (severe) thunderstorm forecast system are good, and, therefore, the system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in April 2004.

scholarly journals Resolution Dependence of European Precipitation in a State-of-the-Art Atmospheric General Circulation Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5134-5149 ◽  
Author(s):  
Ronald van Haren ◽  
Reindert J. Haarsma ◽  
Geert Jan Van Oldenborgh ◽  
Wilco Hazeleger
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Vertical Velocity ◽  
Moisture Transport ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Accurate Representation ◽  
Medium Resolution ◽  
Resolution Model ◽  
High Resolution Model

Abstract In this study, the authors investigate the effect of GCM spatial resolution on modeled precipitation over Europe. The objectives of the analysis are to determine whether climate models have sufficient spatial resolution to have an accurate representation of the storm tracks that affect precipitation. They investigate if there is a significant statistical difference in modeled precipitation between a medium-resolution (~112-km horizontal resolution) and a high-resolution (~25-km horizontal resolution) version of a state-of-the-art AGCM (EC-EARTH), if either model resolution gives a better representation of precipitation in the current climate, and what processes are responsible for the differences in modeled precipitation. The authors find that the high-resolution model gives a more accurate representation of northern and central European winter precipitation. The medium-resolution model has a larger positive bias in precipitation in most of the northern half of Europe. Storm tracks are better simulated in the high-resolution model, providing for a more accurate horizontal moisture transport and moisture convergence. Using a decomposition of the precipitation difference between the medium- and high-resolution model in a part related and a part unrelated to a difference in the distribution of vertical atmospheric velocity, the authors find that the smaller precipitation bias in central and northern Europe is largely unrelated to a difference in vertical velocity distribution. The smaller precipitation amount in these areas is in agreement with less moisture transport over this area in the high-resolution model. In areas with orography the change in vertical velocity distribution is found to be more important.

scholarly journals High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals

2009 ◽  
Vol 9 (5) ◽  
pp. 20599-20630
Author(s):  
D. Pillai ◽  
C. Gerbig ◽  
J. Marshall ◽  
R. Ahmadov ◽  
R. Kretschmer ◽  
...  
Keyword(s):  
High Resolution ◽  
Horizontal Resolution ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Global Models ◽  
Near Surface ◽  
Temporal Sampling ◽  
Inverse Models ◽  
Representation Error ◽  
Satellite Retrievals

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the bias component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

scholarly journals DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

2019 ◽  
Vol 11 (9) ◽  
pp. 1128 ◽  
Author(s):  
Maryam Rahnemoonfar ◽  
Dugan Dobbs ◽  
Masoud Yari ◽  
Michael J. Starek
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Sparse Matrix ◽  
Aerial Images ◽  
Heat Map ◽  
High Resolution Image ◽  
Current State ◽  
Density Map ◽  
Distinct Features

Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

scholarly journals Modelling CO<sub>2</sub> weather – why horizontal resolution matters

2019 ◽  
Vol 19 (11) ◽  
pp. 7347-7376 ◽  
Author(s):  
Anna Agustí-Panareda ◽  
Michail Diamantakis ◽  
Sébastien Massart ◽  
Frédéric Chevallier ◽  
Joaquín Muñoz-Sabater ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
High Resolution ◽  
Greenhouse Gas ◽  
Real Time ◽  
Horizontal Resolution ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Small Scale ◽  
Co2 Fluxes

Abstract. Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

scholarly journals Seamless multi-model postprocessing for air temperature forecasts in complex topography

2021 ◽  
Author(s):  
Regula Keller ◽  
Jan Rajczak ◽  
Jonas Bhend ◽  
Christoph Spirig ◽  
Stephan Hemri ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Small Scale ◽  
Model Output ◽  
Model Combination ◽  
Single Model ◽  
Short Term ◽  
Time Horizons ◽  
Direct Model ◽  
Medium Range

AbstractStatistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multi-model EMOS approach (’Mixed EMOS’) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8-12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single model postprocessing, it also enables to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity.

scholarly journals Evaluation of high-resolution reanalyses for wind energy application in the North Sea and Baltic Sea

2021 ◽  
Author(s):  
Thomas Spangehl ◽  
Michael Borsche ◽  
Deborah Niermann ◽  
Frank Kaspar ◽  
Birger Tinz
Keyword(s):  
High Resolution ◽  
Baltic Sea ◽  
North Sea ◽  
State Of The Art ◽  
Wind Farms ◽  
Horizontal Resolution ◽  
Offshore Wind ◽  
The North ◽  
Hourly Data ◽  
The North Sea

&lt;p&gt;The exploitation of offshore wind energy is an essential part of the German energy transition (Energiewende). The planning of new offshore wind farms demands detailed information on wind conditions at turbine hub heights in the North Sea and Baltic Sea. High-resolution reanalyses which are based on state-of-the-art numerical weather prediction (NWP) models combined with data assimilation systems offer the required meteorological data which are suitable for climatological assessment.&lt;/p&gt;&lt;p&gt;The regional reanalysis COSMO-REA6 operated by Germany&amp;#8217;s national meteorological service (Deutscher Wetterdienst, DWD) provides hourly data of 6 km horizontal resolution for 1995-2019/08 (Kaspar et al., 2020). Moreover, hourly data of 31 km horizontal resolution for 1950 to present are available from the global reanalysis ERA5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). DWD delivers reanalysis data and statistical evaluation results to Bundesamt f&amp;#252;r Seeschifffahrt und Hydrographie (BSH) in order to facilitate offshore site tenders. Data and a report were recently published as part of the tenders for 2021 (https://pinta.bsh.de/).&lt;/p&gt;&lt;p&gt;Here we present an evaluation of the 100 m wind speed and direction from COSMO-REA6 and ERA5 based on a comprehensive statistical analysis. On the reference side the FINO measurements (Research platforms in the North Sea and Baltic Sea, https://www.fino-offshore.de/en/index.html) from FINO1 and FINO2 are used. The FINO measurements are not used by the data assimilation schemes of the two reanalyses and therefore constitute independent reference data. The focus is on episodes prior to the installation of wind farms in the direct vicinity of the FINO platforms to avoid wake effects. The quality of the two reanalyses is compared to other state-of-the-art reanalyses and wind atlas data.&lt;/p&gt;&lt;p&gt;Reference:&lt;/p&gt;&lt;p&gt;Kaspar et al. (2020): Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy, Adv. Sci. Res., 17, 115&amp;#8211;128, https://doi.org/10.5194/asr-17-115-2020.&lt;/p&gt;

scholarly journals Object Detection and Tracking using YOLO v3 Framework for Increased Resolution Video

2020 ◽  
Vol 9 (6) ◽  
pp. 118-125
Keyword(s):  
Operating System ◽  
High Resolution ◽  
Real Time ◽  
The Other ◽  
Video Frame ◽  
Model Output ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Feature Extractor ◽  
Classified Image

The proposed system is used for vehicle detection and tracking from the high-resolution video. It detects the object (vehicles) and recognizes the object comparing its features with the features of the objects stored in the database. If the features match, then object is tracked. There are two steps of implementation, online and offline process. In offline process the data in the form of images are given to feature extractor and then after to the trained YOLO v3 model and weight files is generated form the pre-trained YOLO v3 model. In online phase, real-time video is applied to feature extractor to extract the features and then applied to the pre-trained YOLO v3 model. The other reference to YOLO v3 model pre-trained is the output of weight file. The YOLO v3 model process on the video frame and weight file extracted features, the model output is classified image. In YOLO v3 Darknet-53 is used along with Keras, some libraries with OpenCV, Tensor Flow, and Numpy. The proposed system is implemented on PC Intel Pentium G500, 8GB and operating system Windows 7 is used for processing our system. The system is tested on PASCAL VOC dataset and the results obtained are accuracy 80%, precision 80%, recall 100%, F1-Score 88%, mAP 76.7%, and 0.018%. The system is implemented using python 3.6.0 software and also tested using real-time video having 1280x720 and 1920x1080 resolutions. The execution time for one frame of video having resolution of 1280x720 (HD) and 1920x1080 (FHD) and 1280x720 (HD) are 1.840 second and 4.414808 seconds respectively with accuracy is 80%.

scholarly journals High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals

2010 ◽  
Vol 10 (1) ◽  
pp. 83-94 ◽  
Author(s):  
D. Pillai ◽  
C. Gerbig ◽  
J. Marshall ◽  
R. Ahmadov ◽  
R. Kretschmer ◽  
...  
Keyword(s):  
High Resolution ◽  
Horizontal Resolution ◽  
Systematic Bias ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Global Models ◽  
Near Surface ◽  
Inverse Models ◽  
Representation Error ◽  
Satellite Retrievals

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

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