Feasibility of assimilating ASCAT surface winds into a Limited Area Model

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.

On the charging state of atmospheric aerosols and ion-induced nucleation

Floating in the air that surrounds us is a number of small particles, invisible to the human eye. The mixture of air and particles, liquid or solid, is called an aerosol. Aerosols have significant effects on air quality, visibility and health, and on the Earth's climate. Their effect on the Earth's climate is the least understood of climatically relevant effects. They can scatter the incoming radiation from the Sun, or they can act as seeds onto which cloud droplets are formed. Aerosol particles are created directly, by human activity or natural reasons such as breaking ocean waves or sandstorms. They can also be created indirectly as vapors or very small particles are emitted into the atmosphere and they combine to form small particles that later grow to reach climatically or health relevant sizes. The mechanisms through which those particles are formed is still under scientific discussion, even though this knowledge is crucial to make air quality or climate predictions, or to understand how aerosols will influence and will be influenced by the climate's feedback loops. One of the proposed mechanisms responsible for new particle formation is ion-induced nucleation. This mechanism is based on the idea that newly formed particles were ultimately formed around an electric charge. The amount of available charges in the atmosphere varies depending on radon concentrations in the soil and in the air, as well as incoming ionizing radiation from outer space. In this thesis, ion-induced nucleation is investigated through long-term measurements in two different environments: in the background site of Hyytiälä and in the urban site that is Helsinki. The main conclusion of this thesis is that ion-induced nucleation generally plays a minor role in new particle formation. The fraction of particles formed varies from day to day and from place to place. The relative importance of ion-induced nucleation, i.e. the fraction of particles formed through ion-induced nucleation, is bigger in cleaner areas where the absolute number of particles formed is smaller. Moreover, ion-induced nucleation contributes to a bigger fraction of particles on warmer days, when the sulfuric acid and water vapor saturation ratios are lower. This analysis will help to understand the feedbacks associated with climate change.

Categorical Meteorological Products: Evaluation and Analysis

In meteorology, observations and forecasts of a wide range of phenomena for example, snow, clouds, hail, fog, and tornados can be categorical, that is, they can only have discrete values (e.g., "snow" and "no snow"). Concentrating on satellite-based snow and cloud analyses, this thesis explores methods that have been developed for evaluation of categorical products and analyses. Different algorithms for satellite products generate different results; sometimes the differences are subtle, sometimes all too visible. In addition to differences between algorithms, the satellite products are influenced by physical processes and conditions, such as diurnal and seasonal variation in solar radiation, topography, and land use. The analysis of satellite-based snow cover analyses from NOAA, NASA, and EUMETSAT, and snow analyses for numerical weather prediction models from FMI and ECMWF was complicated by the fact that we did not have the true knowledge of snow extent, and we were forced simply to measure the agreement between different products. The Sammon mapping, a multidimensional scaling method, was then used to visualize the differences between different products. The trustworthiness of the results for cloud analyses [EUMETSAT Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS)] compared with ceilometers of the Helsinki Testbed was estimated by constructing confidence intervals (CIs). Bootstrapping, a statistical resampling method, was used to construct CIs, especially in the presence of spatial and temporal correlation. The reference data for validation are constantly in short supply. In general, the needs of a particular project drive the requirements for evaluation, for example, for the accuracy and the timeliness of the particular data and methods. In this vein, we discuss tentatively how data provided by general public, e.g., photos shared on the Internet photo-sharing service Flickr, can be used as a new source for validation. Results show that they are of reasonable quality and their use for case studies can be warmly recommended. Last, the use of cluster analysis on meteorological in-situ measurements was explored. The Autoclass algorithm was used to construct compact representations of synoptic conditions of fog at Finnish airports.