Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems

A Valid Time Shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1-2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3×36 VTS), and as a cost-savings method (3×12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3×36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3×12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3×36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3×12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3×12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3x36 VTS and 60 – 120 min for 3×12 VTS. The best 3×36 VTS experiments add a computational cost of 45-67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3×12 VTS experiments save about 24-41% costs over ENS-36.

Simulationen von historischen Stürmen und Sturmfluten in der Deutschen Bucht

<p>Sturmfluten sind gekennzeichnet durch das gleichzeitige Auftreten von Tidehochwasser und eines hohen Windstaus. Im letzten Jahrhundert gab es verschiedene schwere Sturmereignisse, die teilweise starke Schäden an den Küsten der Deutschen Bucht verursachten.</p> <p>Wir haben drei verschiedene historische Sturmereignisse herausgesucht, die sich in ihrer Zugbahn und Tide unterscheiden.</p> <ul> <li>Für die ostfriesische Nordseeküste gilt die Sturmflut vom 13.03.1906 heute noch als Bemessungsgrundlage für den Küstenschutz. Bisher war es nicht möglich, mangels atmosphärischen Antriebsdaten, diese Sturmflut zu simulieren.</li> <li>Der Sturm vom 10.02.1949 ereignete sich während Tideniedrigwasser und erzeugte einen hohen Windstau. An der Pegelstation Husum wurde das höchste und in Cuxhaven das dritthöchste Niedrigwasser gemessen.</li> <li>Die schwere Sturmflut von 16./17.02.1962 verursachte starke Schäden an der deutschen Nordseeküste und Hamburg.</li> </ul> <p> </p> <p>In unseren Untersuchungen haben wir folgende Fragestellungen behandelt:</p> <ul> <li>Können mit den atmosphärischen Daten aus dem 20th Century Reanalysis Project die Stürme simuliert werden?</li> <li>Können wir mit einem hydrodynamischen Modell die Wasserstände dieser Sturmereignisse simulieren?</li> <li>Wieviel höher wäre der Wasserstand, wenn die Stürme bei Springtide stattgefunden hätte?</li> </ul> <p> </p> <p>Für die Beantwortung dieser Fragen wurde das hydrodynamische TRIM-NP-Modell mit Druck- und Winddaten aus dem 20th Century Reanalysis Project (20CR) angetrieben und die daraus resultierenden Wasserstände mit Messungen an Pegelstationen verglichen. An den seitlichen Rändern des TRIM-NP-Modells wurden Daten aus dem FES2004 Tide-Modell verwendet.</p> <p>Für die Sturmflut von 1906 wurde eine synoptische Rekonstruktion basierend auf historische Druckdaten angefertigt und die Windgeschwindigkeit daraus berechnet. </p> <p>Erste Ergebnisse zeigen, dass für die einzelnen Sturmereignisse die Wasserstände, angetrieben durch einzelne Ensemble Member aus 20CR-Reanalysedaten, gut mit den Messungen übereinstimmen. Durch die Verschiebung der Tide erhöhen sich die Wasserstände für die Sturmereignisse 1949 und 1962 um einige Dezimeter, das heißt, dass die Sturmfluten hätten noch höher auflaufen können.   </p> <p> </p> <p>---------</p> <p>Die Sturmflut im März 1906</p> <p>https://www.dkrz.de/de/projekte-und-partner/HLRE-Projekte/focus/sturmflut1906</p>

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

<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>

How well are hazards associated with derechos reproduced in regional climate simulations?

An 11-member ensemble of convection-permitting regional simulations of the fast-moving and destructive derecho of June 29 – 30, 2012 that impacted the northeastern urban corridor of the US is presented. This event generated 1100 reports of damaging winds, significant wind gusts over an extensive area of up to 500,000 km2, caused several fatalities and resulted in widespread loss of electrical power. Extreme events such as this are increasingly being used within pseudo-global warming experiments that seek to examine the sensitivity of historical, societally-important events to global climate non-stationarity and how they may evolve as a result of changing thermodynamic and dynamic context. As such it is important to examine the fidelity with which such events are described in hindcast experiments. The regional simulations presented herein are performed using the Weather Research and Forecasting (WRF) model. The resulting ensemble is used to explore simulation fidelity relative to observations for wind gust magnitudes, spatial scales of convection (as manifest in high composite reflectivity), and both rainfall and hail production as a function of model configuration (microphysics parameterization, lateral boundary conditions (LBC), start date, and use of nudging). We also examine the degree to which each ensemble member differs with respect to key mesoscale drivers of convective systems (e.g. convective available potential energy and vertical wind shear) and critical manifestations of deep convection; e.g. vertical velocities, cold pool generation, and how those properties relate to correct characterization of the associated atmospheric hazards (wind gusts and hail). Here, we show that the use of a double-moment, 7-class scheme with number concentrations for all species (including hail and graupel) results in the greatest fidelity of model simulated wind gusts and convective structure against the observations of this event. We further show very high sensitivity to the LBC employed and specifically that simulation fidelity is higher for simulations nested within ERA-Interim than ERA5.

Bayesian model averaging of the RegCM temperature projections: a Canadian case study

The choices of physical schemes coupled in the regional climate model (RegCM), the input general circulation model (GCM) results, and the emission scenarios may cause considerable uncertainties in future temperature projections. Therefore, the ensemble approach, which can be used to reflect these uncertainties, is highly desired. In this study, the probabilistic projections for future temperature are generated at 88 Canadian climate stations based on the developed RegCM ensemble and obtained Bayesian model averaging (BMA) weights. The BMA weights indicate that the RegCM coupled with the holtslag PBL scheme driven by the HadGEM can provide relatively reliable temperature projections at most climate stations. It is also suggested that the BMA approach is effective in simulating temperature over middle and eastern Canada through taking the advantage of each ensemble member. However, the effectiveness of the BMA method is limited when all the models in the ensemble cannot simulate the temperature robustly. The projected results demonstrate that the temperature will increase continuously in the future, while the temperature increase under RCP8.5 will be significantly larger than that under RCP4.5.

Transient eddy kinetic energetics on Mars in three reanalysis datasets

The ability of Martian reanalysis datasets to represent the growth and decay of short-period (1.5 < P < 8 sol) transient eddies is compared across the Mars Analysis Correction Data Assimilation (MACDA), Open access to Mars Assimilated Remote Soundings (OpenMARS), and Ensemble Mars Reanalysis System (EMARS). Short-period eddies are predominantly surface-based, have the largest amplitudes in the northern hemisphere, and are found, in order of decreasing eddy kinetic energy amplitude, in Utopia, Acidalia, and Arcadia Planitae in the northern hemisphere, and south of the Tharsis Plateau and between Argyre and Hellas Basins in the southern hemisphere. Short-period eddies grow on the upstream (western) sides of basins via baroclinic energy conversion and by extracting energy from the mean flow and long-period (P > 8 sol) eddies when interacting with high relief. Overall, the combined impact of barotropic energy conversion is a net loss of eddy kinetic energy, which rectifies previous conflicting results. When Thermal Emission Spectrometer observations are assimilated (Mars years 24–27), all three reanalyses agree on eddy amplitude and timing, but during the Mars Climate Sounder (MCS) observational era (Mars years 28–33), eddies are less constrained. The EMARS ensemble member has considerably higher eddy generation than the ensemble mean, and bulk eddy amplitudes in the deterministic OpenMARS reanalysis agree with the EMARS ensemble rather than the EMARS member. Thus, analysis of individual eddies during the MCS era should only be performed when eddy amplitudes are large and when there is agreement across reanalyses.

Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations

Via 11 years of high-frequency measurements, we calculated the probability space of expected offshore wind-speed ramps, recasting it compactly in terms of relevant load-driving quantities for horizontal-axis wind turbines. A statistical ensemble of events in reduced ramp-parameter space (ramp acceleration, mean speed after ramp, upper-level shear) was created to capture the variability of ramp parameters and also allow connection of such to ramp-driven loads. Constrained Mann-model (CMM) turbulence simulations coupled to an aeroelastic model were made for each ensemble member, for a single turbine. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % for blade-root flap-wise bending moment and tower-base fore–aft moment, plus ∼ 3 % per 0.1 m/s2 of bulk ramp-acceleration magnitude. The ensemble of ramp events from the CMM was also embedded in large-eddy simulation (LES) of a wind farm consisting of rows of nine turbines. The LES uses actuator-line modeling for the turbines and is coupled to the aeroelastic model. The LES results indicate that the ramps, and the mean acceleration associated with them, tend to persist through the farm. Depending on the ramp acceleration, ramps crossing rated speed lead to maximum loads, which are nearly constant for the third row and further downwind. Where rated power is not achieved, the loads primarily depend on wind speed; as mean winds weaken within the farm, ramps can again have U < Vrated. This leads to higher loads than pre-ramp conditions, with the distance where loads begin to increase depending on inflow Umax⁡ relative to Vrated. For the ramps considered here, the effect of turbulence on loads is found to be small relative to ramp amplitude that causes Vrated to be exceeded, but for ramps with Uafter < Vrated, the combination of ramp and turbulence can cause load maxima. The same sensitivity of loads to acceleration is found in both the CMM-aeroelastic simulations and the coupled LES.

Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Climate change impacts on the Greek tourism sector

&lt;p&gt;In this study we examine the impacts of climate change on the tourism sector using a number of tailored climate indicators assessing whether climate conditions are suitable for touristic activities such as the Tourism Climate Index -focusing on outdoor activities- and the Beach Climate Index -focusing on beach activities- as well as fire danger indicators such as the Canadian Fire Weather Index, focusing on forest fire risk. To this aim daily or sub-daily data for a number of meteorological variables from a large ensemble member of Regional Climate Models from the EURO-CORDEX data base are used. The data cover the period 1971-2100 under three RCP emissions scenarios, namely RCP2.6, RCP4.5 and RCP8.5. The analysis is performed for three periods, the 1971-2000 which is used as a reference period and two future periods, the 2021-2050 and 2071-2100. The results indicate that the robust warming projected on a seasonal basis, under all three climate scenarios, drives the changes on all indicators examined. Regarding the climate suitability indicators for tourism the results indicate a lengthening of the tourist season suitable climate conditions while for the fire danger indicators, an increase in the number of days with high and very high fire danger conditions is projected. The most pronounced changes are evident towards the end of the century and under the RCP8.5 future emissions scenario. This study is performed in the framework of CLIMPACT, a&lt;strong&gt; &lt;/strong&gt;Greek national funded project which aims&lt;strong&gt; &lt;/strong&gt;to immediate integration, harmonization and optimization of existing climate services and early warning systems for climate change-related natural disasters in Greece, including supportive observations from relevant national infrastructure.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing

&lt;p&gt;Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products.&amp;#160;&lt;/p&gt;&lt;p&gt;Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps.&amp;#160;&lt;/p&gt;&lt;p&gt;The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.&lt;/p&gt;