New methods using in-situ and remote-sensing observations for improved meteorological analysis

Observations have been and are an important part of today's meteorological developments. Surface observations are very useful as they are, providing weather information for a point location. ough they do not give much information, if any, on what happens between the stations across a larger area. With models one can create an analysis of the meteorological situation, i.e. calculate and estimate what happens between these fixed observation points. Remote-sensing data, such as radar and satellite, are being processed and the output is given over a domain as an analysed product of their measurements. For example, radar gives a plot of where the rain is located, i.e. an analysis of the current precipitation. With a series of radar images, a human (subjectively) or a computer objectively) can process this information to estimate where the rain will move and be located within the next few minutes (even hours), i.e. a short forecast also called "nowcast". is applies to some extent also for other observations, such as satellite data (cloud propagation). But for most quantities (such as temperature, wind, etc) it is significantly harder to make such a nowcast, since these are influenced by many other factors and there is no linear development of them. Therefore, there are forecast models that solve physical and dynamic equations, so that one can estimate the future weather for the coming hours and days. A prerequisite for generating a forecast of high quality is to capture the initial weather conditions as best as possible. This is done using observations and they are introduced into the forecast model through different techniques, where the model creates its own analysis as the initial step. There remain problems since forecast models often are affected by physical disagreements, as the dynamic conditions are not in balance. This results in the model having a spin-up effect, where the meteorological quantities are not yet in balance with each other and the resulting weather conditions are not always reliable during the first hours. Hence, a lot of research is spent on how to reduce this spin-up effect and on the use of nowcast models, in order to deliver the best model results for the first few hours of the forecast period. In this dissertation, the research work has been to improve the meteorological analysis, algorithms and functionality, using the Local Analysis and Prediction System (LAPS) model. Different kinds of observations were used and their interdependencies have been studied, in order to combine and merge information from variousinstruments. Primarily focus has been to improve the estimation of precipitation accumulation and meteorological quantities that affect wind energy. The LAPS developments have been used for several end-users and nowcasting applications, and experimentally as initial conditions for forecast modelling. The studies have been concentrated on Finland and nearby sea areas, with the available datasets for this domain. By combining surface-station measurements, radar and lightning information, one can improve the precipitation-amount estimations. The use of lightning data further improves the estimates and gives the advantage of having additional data outside radar coverage, which can potentially be very useful for example over sea areas. In addition, the improved LAPS analyses (cloud-related quantities) and a newly developed model (LOWICE), calculating the electricity production during wintertime (taking into account the icing of wind turbine rotor blades which reduces efficiency), have shown good results

The Extratropical Transition of Hurricane Debby (1982) and the Subsequent Development of an Intense Windstorm over Finland

On 22 September 1982, an intense windstorm caused considerable damage in northern Finland. Local forecasters noted that this windstorm potentially was related to Hurricane Debby, a category 4 hurricane that occurred just 5 days earlier. Due to the unique nature of the event and lack of prior research, our aim is to document the synoptic sequence of events related to this storm using ERA-Interim reanalysis data, best track data, and output from OpenIFS simulations. During extratropical transition, the outflow from Debby resulted in a ridge building and an acceleration of the jet. Debby did not reintensify immediately in the midlatitudes despite the presence of an upper-level trough. Instead, ex-Debby propagated rapidly across the Atlantic as a diabatic Rossby wave-like feature. Simultaneously, an upper-level trough approached from the northeast and once ex-Debby moved ahead of this feature near the United Kingdom, rapid reintensification began. All OpenIFS forecasts diverged from reanalysis after only 2 days indicating intrinsic low predictability and strong sensitivities. Phasing between Hurricane Debby and the weak trough, and phasing of the upper- and lower-level potential vorticity anomalies near the United Kingdom was important in the evolution of ex-Debby. In the only OpenIFS simulation to correctly capture the phasing over the United Kingdom, stronger wind gusts were simulated over northern Finland than in any other simulation. Turbulent mixing behind the cold front, and convectively driven downdrafts in the warm sector, enhanced the wind gusts over Finland. To further improve understanding of this case, we suggest conducting research using an ensemble approach.Peer reviewe

Climatology, variability, and trends in near-surface wind speeds over the North Atlantic and Europe during 1979-2018 based on ERA5

This study presents the monthly 10‐m wind speed climatology, decadal variability and possible trends in the North Atlantic and Europe from ERA5 reanalysis from 1979 to 2018 and investigates the physical reasons for the decadal variability. Additionally, temporal time series are examined in three locations: the central North Atlantic, Finland and Iberian Peninsula. The 40‐year mean and the 98th percentile wind speeds emphasize a distinct land‐sea contrast and a seasonal variation with the strongest winds over the ocean and during winter. The strongest winds and the highest variability are associated with the storm tracks and local wind phenomena such as the mistral. The extremeness of the winds is examined with an extreme wind factor (the 98th percentile divided by mean wind speeds) which in all months is higher in southern Europe than in northern Europe. Mostly no linear trends in 10‐m wind speeds are identified in the three locations but large annual and decadal variability is evident. The decadal 10‐m wind speeds were stronger than average in the 1990s in northern Europe and in the 1980s and 2010s in southern Europe. These decadal changes were largely explained by the positioning of the jet stream and storm tracks and the strength of the north–south pressure gradient in the North Atlantic. The 10‐m winds have a positive correlation with the North Atlantic Oscillation in the central North Atlantic and Finland on annual scales and during cold season months and a negative correlation in Iberian Peninsula mostly from July to March. The Atlantic Multi‐decadal Oscillation has a moderate negative correlation with the winds in the central North Atlantic but no correlation in Finland and Iberian Peninsula. Overall, our results emphasize that while linear trends in wind speeds may show a general long‐term trend, more information on the changes is obtained by analysing long‐term variability.This study presents the monthly 10-m wind speed climatology, decadal variability and possible trends in the North Atlantic and Europe from ERA5 reanalysis from 1979 to 2018 and investigates the physical reasons for the decadal variability. Additionally, temporal time series are examined in three locations: the central North Atlantic, Finland and Iberian Peninsula. The 40-year mean and the 98th percentile wind speeds emphasize a distinct land-sea contrast and a seasonal variation with the strongest winds over the ocean and during winter. The strongest winds and the highest variability are associated with the storm tracks and local wind phenomena such as the mistral. The extremeness of the winds is examined with an extreme wind factor (the 98th percentile divided by mean wind speeds) which in all months is higher in southern Europe than in northern Europe. Mostly no linear trends in 10-m wind speeds are identified in the three locations but large annual and decadal variability is evident. The decadal 10-m wind speeds were stronger than average in the 1990s in northern Europe and in the 1980s and 2010s in southern Europe. These decadal changes were largely explained by the positioning of the jet stream and storm tracks and the strength of the north-south pressure gradient in the North Atlantic. The 10-m winds have a positive correlation with the North Atlantic Oscillation in the central North Atlantic and Finland on annual scales and during cold season months and a negative correlation in Iberian Peninsula mostly from July to March. The Atlantic Multi-decadal Oscillation has a moderate negative correlation with the winds in the central North Atlantic but no correlation in Finland and Iberian Peninsula. Overall, our results emphasize that while linear trends in wind speeds may show a general long-term trend, more information on the changes is obtained by analysing long-term variability.Peer reviewe

Impact of strong winds, heavy snow loads and soil frost conditions on the risks to forests in Northern Europe

The aim of this work was to study the potential impacts of strong winds, heavy snow loads, and soil frost conditions on the risks to forests in northern Europe under the current and changing climate (until 2100), with the main focus on Finland. More specifically, the analyses concentrated on: i) changes in the occurrence of strong winds, heavy snow loads, and unfrozen soil conditions in Finland, ii) regional risks to Finnish forests from heavy snow loads and strong winds, iii) the mean and extreme geostrophic wind speeds in Northern Europe, and iv) estimation of windstorm-related timber losses in Europe in 1960-2011 using the geostrophic and ageostrophic isallobaric winds as a basis for the analyses. This work employed the meteorological measurements made by the Finnish Meteorological Institute (FMI) between 1961-2009, the datasets from the Finnish National Forest Inventory and the EFIATLANTIC storm catalogue, a number of global climate model (GCM) simulation runs using different greenhouse gas (GHG) emission scenarios (A1B, A2, and B1) and re-analyzed weather datasets ERA-40 and ERA-Interim. The occurrence and depths of soil frost were studied with a model that simulates the freezing of the snow-free ground. The occurrence of large snow loads was estimated with the cumulative snow load approach. The analyses of the risks from snow and/or wind to forests were based on simulations done by the ecosystem model SIMA, the mechanistic model HWIND, and a new regression fit between storm wind impact and timber losses in Europe. According to this work, the growth of Finnish forests is expected to increase under the changing climate. Concurrently, the tree species distribution may also change, which affects the potential risks to forests from wind and snow. In the current climate, strong mean wind speeds of about 17-18 m s-1 have occurred approximately once in 10 years in Finland (in October-February) and caused wind damages. In this work, wind speeds induced by intense cyclones were also found to correlate well with primary losses of timber. The annual maximum soil frost depth has also decreased 5-10% in the period 1980-2009 compared to the period 1971-2000 in southern and central Finland. Under the future climate projections, the soil is expected to hardly freeze at all in southern and central Finland by 2100. Mean and extreme geostrophic wind speeds are also projected to increase during September-April slightly by 2100. Furthermore, days with heavy snow loads may still occur in the near future. To conclude, wind-induced risks to forests in particular may increase in Finland and elsewhere in Northern Europe in the forthcoming decades. However, proper and timely management of forests could help reduce at least to some degree wind-induced risks to forests in the future.Tämän työn tavoitteena oli tutkia voimakkaiden tuulten, lumikuormien ja roudan esiintymisen mahdollisia vaikutuksia metsien tuhoriskeihin Pohjois-Euroopassa. Tarkastelut tehtiin nyky- ja muuttuvalle ilmastolle aina vuoteen 2100 asti. Pääpaino tutkimuksessa oli Suomessa. Työssä analysoitiin i) voimakkaan tuulen, lumikuormien ja routaolojen esiintymisessä tapahtuvia muutoksia Suomessa, ii) Suomen metsiin kohdistuvia alueellisia tuuli- ja lumituhoriskejä, iii) ilmastonmuutoksen vaikutusta keskimääräisiin ja äärimmäisiin geostrofisiin tuuliin Pohjois-Euroopassa ja iv) Euroopan primääristen metsätuhojen ja myrskyissä esiintyneiden geostrofisten ja ageostrofisen isallobaaristen tuulten välistä yhteyttä 1960–2011. Työssä käytettiin Ilmatieteen laitoksen havaintoaineistoa 1961–2009, Metsäntutkimuslaitoksen valtakunnan metsien inventointitietoja (METLA), Eurooppalaista metsien myrskytuhotietokantaa (EFIATLANTIC) ja lukuisia maailmanlaajuisia ilmastomalleja huomioiden eri skenaariot (A1B, A2 ja B1). Tämän lisäksi käytettiin uusanalysoituja meteorologisia aineistoja (reanalyysejä) ERA-40 ja ERA-Interim. Roudan syvyyksiä tutkittiin mallilla, joka laskee lumettoman maan jäätymistä. Puiden lumikuormien esiintymistä arvioitiin kumulatiivisella lumikuormamallilla. Metsiin kohdistuvia lumikuormien ja tuulten aiheuttamia riskejä analysoitiin SIMA-ekosysteemimallilla ja mekanistisella HWIND-mallilla. Tämän lisäksi kehitettiin regressioyhtälö, jolla voitiin arvioida metsien puustotuhojen määrää myrskytuulten perusteella Euroopassa. Työn perusteella Suomen metsien kasvu tulee lisääntymään ilmaston lämmetessä ja lisäksi puulajien jakauma voi muuttua. Tällä voi olla vaikutusta siihen, millaisia lumi- ja tuulituhoja metsissä tulevaisuudessa mahdollisesti esiintyy. Nykyilmastossa, 10-minuutin keskituuli, joka esiintyy keskimäärin kerran kymmenessä vuodessa, on voimakkaimmillaan noin 17–18 ms-1 maan etelä- ja länsiosan sisämaassa. Tyypillisimmin voimakkaita tuulia esiintyy lokakuusta helmikuuhun ulottuvalla jaksolla. Euroopan myrskyjen laajuudella, tuulen nopeudella, ja primääristen metsätuhojen suuruudella on selvä korrelaatio. Suomen etelä- ja keskiosassa roudan syvyydet ovat pienentyneet talvella 5-10 % verrattaessa jaksoa 1980–2009 jaksoon 1971–2000. Mikäli ilmaston lämpeneminen jatkuu, maan etelä- ja keskiosa muuttuvat vuosisadan loppuun mennessä vähitellen roudattomiksi. Lumikuormat voivat vielä lähivuosikymmeninä olla suuria maan etelä- ja keskiosassa, mutta loppuvuosisadalla suuria lumikuormia esiintynee enää pohjoisessa. Suomessa keskimääräiset ja äärimmäiset geostrofiset tuulet voimistuvat syyskuu-huhtikuu välisenä aikana vuoteen 2100 mennessä. Muutos on kuitenkin vain muutaman prosentin luokkaa. Kaikki tulokset vahvistavat käsitystä siitä, että erityisesti metsiin kohdistuva tuuliriski on mitä todennäköisimmin kasvamassa niin Suomessa kuin muualla Pohjois-Euroopassa. Tämä edellyttää tuhoriskien huomioimista metsien hoidossa

Tykkylumen alueellinen esiintyminen Suomessa kahden laskentamenetelmän perusteella

TutkimusselosteSeloste julkaisusta: Lehtonen, I., Hoppula, P., Pirinen, P. & Gregow, H. 2014. Modelling crown snow loads in Finland: a comparison of two methods. Silva Fennica 48(3), article id 1120

Overadaptation to Climate Change? The Case of the 2013 Finnish Electricity Market Act

In this paper, we put forward a definition of over-adaptation in disaster risk reduction (DRR) and climate change adaptation (CCA) projects. We detail an illustrative case in which the response to extreme weather risk while aligned with the goals of CCA, is implemented beyond the economically efficient scale. We undertake a cost-benefit analysis of the 2013 Finnish Electricity Market Act, enacted partially as a reaction to long, storm-induced electricity blackouts experienced after 2000. The Act imposes strict requirements on electricity distribution companies as regards the duration of blackouts. Meeting these requirements entails investments amounting to billions of euros. As a benefit, we quantify the avoided cost from the blackouts for households and producers. Our results, derived from Monte-Carlo simulations, show that for urban areas, the net expected value is positive. However, in rural areas less strict requirements could have been economically more efficient. Our results indicate that distributional impacts and correspondence between those who benefit and those who pay the costs should be taken into account in DRR and CCA policies that require large-scale investments. We also note that the population affected by a disaster may not accept DRR and CCA that are successful in terms of regulation and implementation. This applies when societal and individual preferences do not coincide.Peer reviewe

Characteristics of extratropical cyclones and precursors to windstorms in northern Europe

Peer reviewe