ASCAT pintatuulihavaintojen käyttökelpoisuus rajatun alueen sääennustemallissa

Sea-surface wind observations of previous generation scatterometers have been successfully assimilated into Numerical Weather Prediction (NWP) models. Impact studies conducted with these assimilation implementations have shown a distinct improvement to model analysis and forecast accuracies. The Advanced Scatterometer (ASCAT), flown on Metop-A, offers an improved sea-surface wind accuracy and better data coverage when compared to the previous generation scatterometers. Five individual case studies are carried out. The effect of including ASCAT data into High Resolution Limited Area Model (HIRLAM) assimilation system (4D-Var) is tested to be neutral-positive for situations with general flow direction from the Atlantic Ocean. For northerly flow regimes the effect is negative. This is later discussed to be caused by problems involving modeling northern flows, and also due to the lack of a suitable verification method. Suggestions and an example of an improved verification method is presented later on. A closer examination of a polar low evolution is also shown. It is found that the ASCAT assimilation scheme improves forecast of the initial evolution of the polar low, but the model advects the strong low pressure centre too fast eastward. Finally, the flaws of the implementation are found small and implementing the ASCAT assimilation scheme into the operational HIRLAM suite is feasible, but longer time period validation is still required.Edellisen sukupolven skatterometrien merenpinnan tuulihavaintoja on tuloksellisesti assimiloitu numeerisiin sääennustemalleihin. Näistä assimilaatiototeutuksista tehdyt vaikutustutkimukset ovat osoittaneet selviä parannuksia mallien analyysi- ja ennustetarkkuuksiin. Metop-A:n hyötykuormana oleva kehittynyt skatterometri (ASCAT) tarjoaa tarkempia havaintoja merenpintatuulista sekä kattaa mittauksillaan suuremman alueen edellisiin skatterometreihin verrattuna. ASCAT:in havaintojen assimilaatiota korkean resoluution rajatun alueen malliin (HIRLAM 4D-Var) tutkitaan viidessä erillisessä tapaustutkimuksessa. Vaikutuksen huomataan olevan neutraali-positiivinen tapauksille, joissa yleinen virtaussuunta on Atlantilta, mutta pohjoisille polaarivirtauksille vaikutuksen havaitaan olevan negatiivinen. Tämän päätellään johtuvan ongelmista pohjoisten virtausten mallintamisessa, mutta myös sopivien verifikaatiometodien puutteellisuudesta. Ehdotuksia sekä esimerkki verifikaatiometodien kehittämisestä esitellään myöhemmässä vaiheessa. Työssä tarkastellaan myös lähemmin polaarimatalatapausta. Tarkastelusta selviää, että ASCAT:in assimilaatio analyysijärjestelmään parantaa polaarimatalan alkukehityksen ennustetta, mutta mallin dynamiikka/fysiikka advektoi tämän voimakkaan matalapainekeskuksen liian nopeasti itään. Assimilaation toteutuksen viat havaitaan kuitenkin pieniksi ja ASCAT-havaintojen assimilaation operatiiviseen HIRLAM sääennustemalliin nähdään olevan toteuttamiskelpoinen, tosin pidemmän aikavälin validoinnin tarve nousee vielä esiin

Parametric Uncertainty in Numerical Weather Prediction Models

Numerical Weather Prediction (NWP) models form the basis of weather forecasting. The accuracy of model forecasts can be enhanced by providing a more accurate initial state for the model, and by improving the model representation of relevant atmospheric processes. Modelling of subgrid-scale physical processes causes additional uncertainty in the forecasts since, for example, the rates at which parts of the physical processes occur are not exactly known. The efficiency of these sub-processes in the models is controlled via so called closure parameters. This thesis is motivated by a practical need to estimate the values of these closure parameters objectively, and to assess the uncertainties related to them. In this thesis the Ensemble Prediction and Parameter Estimation System (EPPES) is utilised to determine the optimal closure parameter values, and to learn about their uncertainties. Closure parameters related to convective processes, formation of convective rain and stratiform clouds are studied in two atmospheric General Circulation Models (GCM): the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the ECMWF model HAMburg version (ECHAM5). The parameter estimation is conducted by launching ensembles of medium range forecasts with initial time parameter variations. The fit of each ensemble member to analyses is then evaluated with respect to a target criterion, and the likelihoods of the forecasts are discerned. The target criterion is first set to be 500 hPa level geopotential height Mean Squared Error (MSE) at forecast days three and ten. After the proof of concept experimentations, the use of total energy norm as the target criterion is explored. EPPES estimation with both likelihoods results in parameter values converging to more optimal values during a three-month sampling period. The improved forecast accuracy of the models with the new parameter values are verified through headline skill scores (Root Mean Square Error (RMSE) and Anomaly Correlation Coefficient (ACC)) of 500 hPa geopotential height and a scorecard consisting of multiple model fields. The sampling process also provides information about parameter uncertainties. Three uses for the uncertainty data are highlighted: (i) parametrization deficiencies can be identified from large parameter uncertainties, (ii) parameter correlations can indicate a need for the coupling of parameters, and (iii) adding parameter variations into an ensemble prediction system (EPS) can be used to increase the ensemble spread. The relationship between medium range forecasts and model climatology is explored, too. Closure parameter modification induced cloud cover changes at forecast day three carry over to the very long range forecasts as well. This link could be used to improve model climatology by enhancing the computationally cheaper medium range forecast skill of the model.Jokapäiväisten sääennusteiden pohjana ovat numeeristen sääennustemallien tuottamat ennusteet ilmakehän tulevasta tilasta. Mallien ennustetarkkuutta voidaan parantaa tarkentamalla mallille syötettävää ilmakehän alkutilaa tai mallintamalla ilmakehän ilmiöt realistisemmin. Hilaväliä pienempien ilmiöiden kuvaaminen malleissa tuottaa ennusteisiin oman epävarmuutensa, mm. koska näihin ilmiöihin liittyvien prosessien tehokkuutta ei tiedetä tarkasti. Malleissa näiden aliprosessien nopeutta säädelläään ns. sulkuparametrien kautta. Tämän väitöskirjan tavoitteena on sulkuparametrien arvojen objektiivinen valinta sekä niihin liittyvien epävarmuuksien selvittäminen. Tässä väitöskirjassa parametrien optimaalisten arvojen ja niiden epävarmuuksien estimointi suoritetaan EPPES (Ensemble Prediction and Parameter Estimation System) -algoritmilla. Konvektioon, konvektiiviseen sateeseen ja kerrospilvien muodostumiseen liittyviä parametrejä tutkitaan kahdella globaalilla ilmakehämallilla: Euroopan keskipitkien sääennusteiden keskuksen (ECMWF) IFS (Integrated Forecasting System) -sääennustemallilla ja ECHAM5 (ECMWF model HAMburg version) -ilmastomallilla. Parametrien estimointia varten niiden arvoja muunnellaan keskipitkien sääennusteiden ryväsennustejärjestelmässä. Jokaisen ryppään jäsenen ennustetta verrataan analyysikenttään ja ennusteen osuvuus mitataan ennalta määrätyllä kohdefunktiolla. Kohdefunktiona käytetään ensimmäiseksi 500 hPa painepinnan geopotentiaalikorkeuden MS (Mean Squared) -virhettä kolmen ja 10 päivän sääennusteissa ja EPPES-algoritmin todetaan toimivan halutulla tavalla. Tämän jälkeen kohdefunktioksi vaihdetaan ilmakehän kokonaisenergianormi. Kolmen kuukauden otannoissa kummatkin käytetyt kohdefunktiot johtavat parametrien konvergoitumiseen optimoituihin arvoihin. Uusien parametriarvojen todetaan parantavan enuusteita käyttäen validointimenetelminä 500 hPa painepinnan geopotentiaalikorkeudella RMSE (Root Mean Squared Error) ja ACC (Anomaly Correlation Coefficient) arvoja sekä laajoja mallien vertailutaulukoita. Estimoinnin aikana saadaan myös lisää tieto parametreihin liittyvistä epävarmuuksista. Kolme käyttötarkoitusta nostetaan esiin: (i) suuret epävarmuudet parametreissä viittaavat puutteisiin parametrisaatioissa, (ii) voimakkaat parametrien korrelaatiot ilmaisevat tarpeesta parametrien yhdistämiseksi ja (iii) parametrivariaatioiden lisääminen ryväsennustejärjestelmään kasvattaa järjestelmän ryväshajontaa. Viimeiseksi selvitetään yhteyttä keskipitkien ennusteiden ja mallin klimatologian välillä. Parametrien vaihtamisen aiheuttamien pilvisyyden muutosten rakenne kolmen päivän ennusteissa on havaittavissa myös mallin pitkissä vuosittaisennusteissa. Näin ollen mallin klimatologiaa voisi parantaa myös tarkentamalla mallin ennustuskykyä laskennallisesti halvemmissa keskipitkissä sääennusteissa

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called filter likelihood score which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme. Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory.Peer reviewe

Ensemble prediction using a new dataset of ECMWF initial states - OpenEnsemble 1.0

Ensemble prediction is an indispensable tool in modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has almost exclusively been carried out by operational weather agencies to date. Thus, it has been very hard for academia to contribute to this important branch of NWP research using realistic weather models. In order to open ensemble prediction research up to the wider research community, we have recreated all 50 + 1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (Open Ensemble 1.0) is available for use under a Creative Commons licence and is downloadable from an https server. The dataset covers 1 year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (T(L)159, T(L)399, and T(L)639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of Typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside of major global weather forecasting centres.Peer reviewe

EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average −0.09 W m−2 with a standard deviation due to interannual variability of 0.25 W m−2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘C with an interannual standard deviation of 0.17 ∘C, exhibiting a small drift of 0.015 ± 0.005 ∘C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘C, and its transient climate response is estimated at 2.1 ∘C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.05 ∘C with a standard deviation across ensemble members of 0.35 ∘C in the Northern Hemisphere and 1.29 ± 0.02 ∘C with a corresponding standard deviation of 0.05 ∘C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘C above the preindustrial mean. A 0.5 ∘C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘C

EC-Earth3-AerChem : a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average 0.09 Wm(-2) with a standard deviation due to interannual variability of 0.25 Wm(-2), showing no significant drift. The global surface air temperature in the simulation is on average 14.08 degrees C with an interannual standard deviation of 0.17 degrees C, exhibiting a small drift of 0.015 +/- 0.005 degrees C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 degrees C, and its transient climate response is estimated at 2.1 degrees C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995-2014 has an average bias of -0.86 +/- 0.05 degrees C with a standard deviation across ensemble members of 0.35 degrees C in the North-ern Hemisphere and 1.29 +/- 0.02 degrees C with a corresponding standard deviation of 0.05 degrees C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091-2100) of 4.9 degrees C above the preindustrial mean. A 0.5 degrees C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 degrees C.Peer reviewe

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.Peer reviewe

Algorithmic tuning of spread-skill relationship in ensemble forecasting systems

Abstract In ensemble weather prediction systems, ensemble spread is generated using uncertainty representations for initial and boundary values as well as for model formulation. The ensuing ensemble spread is thus regulated through, what we call, ensemble spread parameters. The task is to specify the parameter values such that the ensemble spread corresponds to the prediction skill of the ensemble mean - a prerequisite for a reliable prediction system. In this paper, we present an algorithmic approach suitable for this task consisting of a differential evolution algorithm with filter likelihood providing evidence. The approach is demonstrated using an idealized ensemble prediction system based on the Lorenz--Wilks system. Our results suggest that it might be possible to optimize the spread parameters without manual intervention. This article is protected by copyright. All rights reserved.Peer reviewe

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called filter likelihood score which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme. Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory