Nutzerorientierte Klimavorhersageprodukte des Deutschen Wetterdiensts – auf dem Weg zu höherer räumlicher Auflösung und verbesserter Vorhersagegüte

2021 ◽  
Author(s):  
Amelie Hoff ◽  
Philip Lorenz ◽  
Clementine Dalelane ◽  
Alexander Pasternack ◽  
Birgit Mannig ◽  
...  
Keyword(s):  
Ensemble Member ◽  
Bias Drift

<p>Es besteht ein wachsender Bedarf an hochaufgelösten Klimavorhersagen der kommenden Wochen, Monate und Jahre. Um diesen Bedarf zu bedienen, veröffentlicht der Deutsche Wetterdienst (DWD) operationell saisonale und dekadische Klimavorhersagen. Daneben ist zukünftig auch die Bereitstellung von postprozessierten EZMW Witterungsvorhersagen geplant. Als gemeinsame Plattform dafür dient die neue DWD Klimavorhersagen-Webseite www.dwd.de/klimavorhersagen, auf der Klimavorhersagen über alle verfügbaren räumlichen und zeitlichen Skalen hinweg konsistent dargestellt werden. </p> <p>Um die räumliche Auflösung und die Vorhersagegüte der Klimavorhersagen zu erhöhen, werden verschiedene Nachbereitungsverfahren angewendet. So wird das am DWD entwickelte empirisch-statistische Downscalingverfahren EPISODES angewendet, um die grobe räumliche Auflösung der globalen Klimavorhersagen für die Region Deutschland auf rund 20 km Gitterweite zu verbessern und Klimavorhersagen für ausgewählte Städte auf Basis von rund 5 km Gitterweite erstellen zu können. Dazu werden statistische Beziehungen zwischen großräumigen Einflussvariablen, wie zum Beispiel dem Luftdruck, und kleinräumigen Zielvariablen, wie dem Niederschlag, mithilfe von hochaufgelösten Beobachtungsdaten verwendet. </p> <p>Die dekadischen Klimavorhersagen werden seit 2021 operationell für die Klimavorhersagen-Webseite statistisch rekalibriert, um Bias, Drift und Ensembledispersion zu korrigieren, so dass Verlässlichkeit und Schärfe der probabilistischen Vorhersage optimiert werden. Für die saisonale Klimavorhersage der Wintermonate wird die Möglichkeit einer Verbesserung der Vorhersagegüte mithilfe einer statistisch selektierten Klimavorhersage untersucht. Bei dieser hybriden saisonalen Klimavorhersage werden einzelne Ensemble Member ausgewählt basierend auf der statistischen Vorhersage der europäischen Luftzirkulation im Winterhalbjahr. Für die statistische Vorhersage werden verschiedene Prädiktoren aus ERA5T Reanalysedaten verwendet, wie zum Beispiel Meeresoberflächentemperatur, Meereis oder die Temperatur in 100 hPa. </p> <p>Neben der statistisch selektierten Wintervorhersage ist zudem die Weiterentwicklung des Downscalingverfahrens EPISODES geplant, sodass es auf eine größere Region angewendet werden kann, die auch den Alpenraum und an Deutschland angrenzende Flusseinzugsgebiete beinhaltet. Für die künftigen Weiterentwicklungen und Erweiterungen der operationellen Klimavorhersageprodukte besteht ein enger Austausch mit Nutzerinnen und Nutzern aus verschiedenen Sektoren, zum Beispiel im Rahmen des jährlich stattfindenden Nutzerworkshops.</p>

Temperature dependence of insertion loss and bias drift of Ti:LiNbO3 optical external modulator

2002 ◽  
Author(s):  
Hyung-Do Yoon ◽  
Woo-Seok Yang ◽  
Han-Young Lee ◽  
Dae-Won Yoon
Keyword(s):  
Temperature Dependence ◽  
Insertion Loss ◽  
Bias Drift

scholarly journals Ensemble Sensitivity Analysis for Mesoscale Forecasts of Dryline Convection Initiation

2016 ◽  
Vol 144 (11) ◽  
pp. 4161-4182 ◽  
Author(s):  
Aaron J. Hill ◽  
Christopher C. Weiss ◽  
Brian C. Ancell
Keyword(s):  
Sensitivity Analysis ◽  
Time Frame ◽  
Short Wave ◽  
Ensemble Member ◽  
Cold Pool ◽  
Dynamic Features ◽  
Wave Trough ◽  
Antecedent Precipitation ◽  
Non Gaussian ◽  
Convection Initiation

Abstract Two cases of dryline convection initiation (CI) over north Texas have been simulated (3 April 2012 and 15 May 2013) from a 50-member WRF-DART ensemble adjustment Kalman filter (EAKF) ensemble. In this study, ensemble sensitivity analysis (ESA) is applied to a convective forecast metric, maximum composite reflectivity (referred to as the response function), as a simple proxy for CI to analyze dynamic mesoscale sensitivities at the surface and aloft. Analysis reveals positional and magnitude sensitivities related to the strength and placement of important dynamic features. Convection initiation is sensitive to the evolving temperature and dewpoint fields upstream of the forecast response region in the near-CI time frame (0–12 h), prior to initiation. The sensitivity to thermodynamics is also manifest in the magnitude of dewpoint gradients along the dryline that triggers the convection. ESA additionally highlights the importance of antecedent precipitation and cold pool generation that modifies the pre-CI environment. Aloft, sensitivity of CI to a weak short-wave trough and capping inversion-level temperature is coherent, consistent, and traceable through the entire forecast period. Notwithstanding the (often) non-Gaussian distribution of ensemble member forecasts of convection, which violate the underpinnings of ESA theory, ESA is demonstrated to sufficiently identify regions that influence dryline CI. These results indicate an application of ESA for severe storm forecasting at operational centers and forecast offices as well as other mesoscale forecasting applications.

scholarly journals Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

2018 ◽  
Vol 57 (4) ◽  
pp. 1011-1019 ◽  
Author(s):  
H. F. Dacre ◽  
N. J. Harvey
Keyword(s):  
Flow Separation ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Forecast Accuracy ◽  
Large Error ◽  
Ensemble Member ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Wind Fields ◽  
Ensemble Forecasts

ABSTRACTVolcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow‐separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble‐member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

scholarly journals Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Bibiana Rodrigues Colossi ◽  
Carlos Eduardo Morelli Tucci
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Computing Time ◽  
Ensemble Member ◽  
Lead Times ◽  
Hydrologic Simulation ◽  
The Mean ◽  
Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

scholarly journals On ozone trend detection: using coupled chemistry–climate simulations to investigate early signs of total column ozone recovery

2018 ◽  
Vol 18 (10) ◽  
pp. 7625-7637 ◽  
Author(s):  
James Keeble ◽  
Hannah Brown ◽  
N. Luke Abraham ◽  
Neil R. P. Harris ◽  
John A. Pyle
Keyword(s):  
Ensemble Member ◽  
Trend Detection ◽  
Large Sample Size ◽  
Total Column Ozone ◽  
Large Variability ◽  
Ozone Trend ◽  
Column Ozone ◽  
The Tropics ◽  
Total Column ◽  
Natural Cycles

Abstract. Total column ozone values from an ensemble of UM-UKCA model simulations are examined to investigate different definitions of progress on the road to ozone recovery. The impacts of modelled internal atmospheric variability are accounted for by applying a multiple linear regression model to modelled total column ozone values, and ozone trend analysis is performed on the resulting ozone residuals. Three definitions of recovery are investigated: (i) a slowed rate of decline and the date of minimum column ozone, (ii) the identification of significant positive trends and (iii) a return to historic values. A return to past thresholds is the last state to be achieved. Minimum column ozone values, averaged from 60° S to 60° N, occur between 1990 and 1995 for each ensemble member, driven in part by the solar minimum conditions during the 1990s. When natural cycles are accounted for, identification of the year of minimum ozone in the resulting ozone residuals is uncertain, with minimum values for each ensemble member occurring at different times between 1992 and 2000. As a result of this large variability, identification of the date of minimum ozone constitutes a poor measure of ozone recovery. Trends for the 2000–2017 period are positive at most latitudes and are statistically significant in the mid-latitudes in both hemispheres when natural cycles are accounted for. This significance results largely from the large sample size of the multi-member ensemble. Significant trends cannot be identified by 2017 at the highest latitudes, due to the large interannual variability in the data, nor in the tropics, due to the small trend magnitude, although it is projected that significant trends may be identified in these regions soon thereafter. While significant positive trends in total column ozone could be identified at all latitudes by ∼ 2030, column ozone values which are lower than the 1980 annual mean can occur in the mid-latitudes until ∼ 2050, and in the tropics and high latitudes deep into the second half of the 21st century.

The DWD climate prediction website

2021 ◽  
Author(s):  
Andreas Paxian ◽  
Katja Reinhardt ◽  
Birgit Mannig ◽  
Katharina Isensee ◽  
Amelie Krug ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Prediction ◽  
Climate Projections ◽  
Climate Data ◽  
Traffic Light ◽  
Daily Work ◽  
Bias Drift ◽  
Climate Service ◽  
German Cities ◽  
Decadal Predictions

<p>DWD provides operational seasonal and decadal predictions of the German climate prediction system since 2016 and 2020, respectively. We plan to present these predictions together with post-processed ECMWF sub-seasonal forecast products on the DWD climate prediction website www.dwd.de/climatepredictions. In March 2020, this climate service was published with decadal predictions for the coming years; sub-seasonal and seasonal predictions for the coming weeks and months will follow.</p><p>The user-oriented evaluation and design of this climate service has been developed in close cooperation with users from various sectors at workshops of the German MiKlip project and will be consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with the prediction skill for 1-year and 5-year means/ sums of temperature and precipitation for different regions (World, Europe, Germany, German regions).</p><p>For Germany, the statistical downscaling EPISODES was applied to reach high spatial resolution required by several climate data users. Decadal predictions were statistically recalibrated in order to adjust bias, drift and standard deviation and optimize ensemble spread. We used the MSESS and RPSS to evaluate the skill of climate predictions in comparison to reference predictions, e.g. ‘observed climatology’ or ‘uninitialized climate projections’ (which are both applied by users until now as an alternative to climate predictions). The significance was tested via bootstraps.</p><p>Within the ‘basic climate predictions’ section, a user-oriented traffic light indicates whether regional-mean climate predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than reference predictions. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction itself (via the color of dots) and its skill (via the size of dots representing the skill categories of the traffic light). The co-development of this climate prediction application with users from different sectors strongly improves the comprehensibility and applicability by users in their daily work.</p><p>In addition to sub-seasonal and seasonal predictions, plans for future extensions of this climate service include multi-year seasonal predictions, e.g. 5-year summer or winter means, combined products for climate predictions and climate projections, further user-oriented, extreme or large-scale variables, e.g. ENSO, or high-resolution applications for German cities based on statistically downscaled predictions.</p>

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