scholarly journals Verification of Multimodel Ensemble Forecasts of North Atlantic Tropical Cyclones

2017 ◽  
Vol 32 (6) ◽  
pp. 2083-2101 ◽  
Author(s):  
Nicholas M. Leonardo ◽  
Brian A. Colle
Keyword(s):  
North Atlantic ◽  
Ensemble Prediction ◽  
Laboratory Model ◽  
Lead Times ◽  
Forecast System ◽  
Deterministic Models ◽  
Ensemble Forecasts ◽  
The Cross ◽  
Cross Track ◽  
Along Track

Abstract North Atlantic tropical cyclone (TC) forecasts from four ensemble prediction systems (EPSs) are verified using the National Hurricane Center’s (NHC) best tracks for the 2008–15 seasons. The 1–5-day forecasts are evaluated for the 21-member National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS), the 23-member UKMO ensemble (UKMET), and the 51-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, as well as a combination of these ensembles [Multimodel Global (MMG)]. Several deterministic models are also evaluated, such as the Global Forecast System (GFSdet), Hurricane Weather Research and Forecasting Model (HWRF), the deterministic ECMWF model (ECdet), and the Geophysical Fluid Dynamical Laboratory model (GFDL).The ECdet track errors are the smallest on average at all lead times, but are not significantly different from the GEFS and ECMWF ensemble means. All models have a slow bias (90–240 km) in the along-track direction by 120 h, while there is little bias in the cross-track direction. Much of this slow bias is attributed to TCs undergoing extratropical transition (ET). All EPSs are underdispersed in the along-track direction, while the ECMWF is slightly overdispersed in the cross-track direction. The MMG and ECMWF track forecasts have more probabilistic skill than the ECdet and comparable skill to the NHC climatology-based cone forecast. TC intensity errors for the HWRF and GFDL are lower than the coarser models within the first 24 h, but are comparable to the ECdet at longer lead times. The ECMWF and MMG have comparable or better probabilistic intensity forecasts than the ECdet, while the GEFS’s weak bias limits its skill.

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

scholarly journals Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

2013 ◽  
Vol 26 (19) ◽  
pp. 7525-7540 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes ◽  
Jean-Raymond Bidlot ◽  
Ana Carrasco ◽  
Øyvind Saetra
Keyword(s):  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Northeast Atlantic ◽  
The North ◽  
Time Period ◽  
The North Sea ◽  
Mean And Variance ◽  
Buoy Data

Abstract A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the northeast Atlantic, the Norwegian Sea, and the North Sea are computed from archived +240-h forecasts of the ECMWF Ensemble Prediction System (EPS) from 1999 to 2009. Three assumptions are made: First, each forecast is representative of a 6-h interval and collectively the dataset is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which is confirmed by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. Anomaly correlations of 0.20 are found, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the dataset it is also found that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-yr archived period exhibit no significant departures from stationarity compared with a recent reforecast; that is, there is no spurious trend because of model upgrades. The EPS yields significantly higher return values than the 40-yr ECMWF Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) and is in good agreement with the high-resolution 10-km Norwegian Reanalyses (NORA10) hindcast, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it has low bias. Confidence intervals are half the width of those found for ERA-Interim because of the magnitude of the dataset.

Predictability of North Atlantic Tropical Cyclone Activity on Intraseasonal Time Scales

2010 ◽  
Vol 138 (12) ◽  
pp. 4362-4374 ◽  
Author(s):  
James I. Belanger ◽  
Judith A. Curry ◽  
Peter J. Webster
Keyword(s):  
Time Scales ◽  
North Atlantic ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Relative Vorticity ◽  
Vertical Shear ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Easterly Waves ◽  
The North

Abstract Recent work suggests that there may exist skill in forecasting tropical cyclones (TC) using dynamically based ensemble products, such as those obtained from the ECMWF Monthly Forecast System (ECMFS). The ECMFS features an ensemble of 51 coupled ocean–atmosphere simulations integrated to 32 days once per week. Predicted levels of TC activity in the North Atlantic Ocean with these monthly ensemble forecasts is compared with the observed variability during the months of June–October during 2008 and 2009. Results indicate that the forecast system can capture large-scale regions that have a higher or lower risk of TC activity and that it has skill above climatology for the Gulf of Mexico and the “Main Development Region” on intraseasonal time scales. Regional forecast skill is traced to the model’s ability to capture the large-scale evolution of deep-layer vertical shear, the frequency of easterly waves, and the variance in 850-hPa relative vorticity. The predictability of TC activity, along with the forecast utility of the ECMFS, is shown to be sensitive to the phase and intensity of the Madden–Julian oscillation at the time of model initialization.

scholarly journals GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

2016 ◽  
Vol XLI-B8 ◽  
pp. 489-494 ◽  
Author(s):  
Luc Girod ◽  
Christopher Nuth ◽  
Andreas Kääb
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Volume Change ◽  
Thermal Emission ◽  
Aster Data ◽  
Rational Polynomial ◽  
The Cross ◽  
Cross Track ◽  
Along Track

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

scholarly journals GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

2016 ◽  
Vol XLI-B8 ◽  
pp. 489-494
Author(s):  
Luc Girod ◽  
Christopher Nuth ◽  
Andreas Kääb
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Volume Change ◽  
Thermal Emission ◽  
Aster Data ◽  
Rational Polynomial ◽  
The Cross ◽  
Cross Track ◽  
Along Track

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

Understanding the role of the troposphere in the surface impact of the 2018 sudden stratospheric warming event

2021 ◽  
Author(s):  
Juan J. González-Alemán ◽  
Christian M. Grams ◽  
Blanca Ayarzagüena ◽  
Pablo Zurita-Gotor ◽  
Daniela I. V. Domeisen Domeisen ◽  
...  
Keyword(s):  
North Atlantic ◽  
Vortex Breakdown ◽  
Rapid Change ◽  
Polar Vortex ◽  
The Arctic ◽  
Lead Times ◽  
Strong Impact ◽  
Ensemble Forecasts ◽  
Stratospheric Polar Vortex ◽  
The North

&lt;p&gt;Sudden stratospheric warmings (SSWs) are impressive phenomena that consist of a rapid stratospheric polar vortex breakdown. SSWs can have a strong impact on the tropospheric weather and are mainly associated with the negative phases of the Arctic and North Atlantic Oscillations (AO, NAO), and with northern European cold outbreaks, thus causing high societal impact. However, the mechanisms behind the downward impact from the stratosphere are insufficiently understood, especially the role played by the troposphere. In this work, we investigate this coupling and its associated predictability limits by studying the 2018 SSW event.&lt;/p&gt;&lt;p&gt;By analyzing ECMWF 15-day ensemble forecasts and partitioning them into different weather regimes, we search for possible dynamical tropospheric events that may have favored the downward stratosphere-troposphere coupling during and after the SSW. It is found that two cyclogenesis events were the main drivers of the negative NAO pattern associated with a Greenland Blocking, causing a rapid change from prevailing westerlies into a blocked state in the North Atlantic region. Unless these cyclogenesis events are simulated in the forecasts, the prediction of a Greenland Blocking does not become highly prevalent. No important stratospheric differences between WRs were found. A possible oceanic contribution to this blocked state is also found. This work corroborates that individual synoptic events might constitute a &amp;#8220;predictability barrier&quot; for subsequent forecast lead times. It also sheds light, on the specific topic of troposphere-stratosphere coupling.&lt;/p&gt;

scholarly journals ICESat measurement of Greenland ice sheet surface slope and roughness

2005 ◽  
Vol 42 ◽  
pp. 83-89 ◽  
Author(s):  
Donghui Yi ◽  
H. Jay Zwally ◽  
Xiaoli Sun
Keyword(s):  
Three Dimensional ◽  
Greenland Ice Sheet ◽  
Surface Slope ◽  
Radar Altimeter ◽  
Digital Elevation ◽  
The Cross ◽  
Elevation Model ◽  
Track Surface ◽  
Cross Track ◽  
Along Track

AbstractThe Ice, Cloud and land Elevation Satellite (ICESat) in its 8 day repeat orbit mode provided data not only on the along-track surface slope, but also on the cross-track surface slope from adjacent repeat ground tracks. During the first 36 days of operation, four to five such repeat orbits occurred within 1 km in the cross-track direction. This provided an opportunity to use ICESat data to measure surface slope in the cross-track direction at 1 km scale. An algorithm was developed to calculate the cross-track surface slope. Combining the slopes in the cross-track and along-track directions gives a three-dimensional surface slope at 1 km scale. The along-track surface slope and surface roughness at 10km scale are also calculated. A comparison between ICESat surface elevation and a European Remote-sensing Satellite (ERS-1) 5 km digital elevation model shows a difference of 1–2 m in central Greenland where the surface slope is small, and >20m at the edge of Greenland where the surface slope is large. The large elevation difference at the edge is most likely due to the slope-induced error in radar altimeter measurement. Accurate surface slope data from ICESat will help to correct the slope-induced error of radar altimeter missions such as Geosat, ERS-1 and ERS-2.

scholarly journals Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

2017 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Seann Reed ◽  
Peter Ahnert ◽  
Pablo Mendoza ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Warm Season ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the U.S. middle Atlantic region, ranging in size from 381 to 12,362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

scholarly journals Use of Ensemble Forecasts to Investigate Synoptic Influences on the Structural Evolution and Predictability of Hurricane Alex (2016) in the Midlatitudes

2018 ◽  
Vol 146 (10) ◽  
pp. 3143-3162 ◽  
Author(s):  
Juan Jesús González-Alemán ◽  
Jenni L. Evans ◽  
Alex M. Kowaleski
Keyword(s):  
North Atlantic ◽  
Structural Evolution ◽  
Conveyor Belt ◽  
Ensemble Prediction ◽  
Structural Development ◽  
Ensemble Forecasts ◽  
Pressure System ◽  
Ensemble Prediction System ◽  
Factors Affecting ◽  
Composite Maps

Abstract Hurricane Alex was an extremely rare hurricane event, the first North Atlantic hurricane to form in January since 1938. Alex developed from an extratropical low pressure system that formed over the western North Atlantic basin, and then underwent tropical transition after moving to the eastern basin. It subsequently underwent anomalous extratropical transition (ET) just north of the Azores Islands. We examine the factors affecting Alex’s structural evolution and the predictability of that evolution. Potential scenarios of structural development are identified from a 51-member forecast ensemble from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF-EPS), initialized at 0000 UTC 10 January 2016. The EPS forecasts are clustered using a regression mixture model based on the storm’s path through the cyclone phase space. Composite maps constructed from these clusters are used to investigate the role of synoptic-scale features on the evolving structure of Hurricane Alex as it interacted with the midlatitude flow. Results suggest that the crucial factor affecting this interplay was the behavior of a large extratropical cyclone and its associated cold front and likely warm conveyor belt upstream of Alex; the intensity of these structures determined whether Alex underwent a typical cold-core ET (as observed) or a warm-seclusion ET. The clustering and compositing methodology proposed not only provides a nuanced analysis of the ensemble forecast variability, helping forecasters to analyze the predictability of future complex tropical–midlatitude interactions, but also presents a method to investigate probable causes of different processes occurring in cyclones.

scholarly journals Global Ensemble Predictions of 2009’s Tropical Cyclones Initialized with an Ensemble Kalman Filter

2011 ◽  
Vol 139 (2) ◽  
pp. 668-688 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclones ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Central Pressure ◽  
Ensemble Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Track Error

Abstract Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO). The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias. Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

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