Weather
Juuso Paajanen presents the difficulties of using geostationary satellite data in high latitudes.
Parallax shift, Limb cooling, Low sun elevation angle are some of the issues associated with mainly geostationary satellites in the high-latitudes. Some satellite images are shown to demonstrate these issues. Some solutions are suggested here for countering these problems.
This presentation talks about Example of the tools we are using in our operational work.
Pia Isolahtenmaki (stepping in for Robert Makitie) presents how FCI proxy data help in differentiating cloud types in low-cloud conditions.
On the 11th of September an almost stationary frontal system occurred over southern Finland leading to development of a large fog cloud over the Gulf of Finland and the Baltic Sea. The fog later moved slowly over southern Finland, where Finland’s busiest airport, Helsinki-Vantaa airport, is located. The fog cloud that occurred over the Gulf of Finland was hard to spot from SEVIRI images due to different layers of middle and high clouds. When thinking about the future, the question arises, would the FCI have spotted something more in this situation?
Johannes Häkkinen presents forecasting different precipitation types and using satellites together with weather models in aviation.
The presentation will go through different precipitation types, forecasting the precipitation types, and supporting the weather model with satellite images. The precipitation types are estimated from a model sounding by top-down method. Satellite imagery is mainly used for estimation of the cloud top. The focus is on aviation weather and short-term forecasts.
Kerttu shows how the snow and ice cover change during decades according to satellite data.
Snow and ice cover of the Northern Hemisphere (NH) are major factors in the global climate system.
Due to the large area and sparse in situ measurements, snow and ice cover monitoring at the continental scale is only possible from satellites. Estimating snow and ice cover at large scale is important for hydrological and climatological applications. As they greatly influence the entire climate system, changes in snow and ice cover will affect the surface energy balance, which in turn makes snow and ice cover important inputs for climate models.
Snow and ice cover in the Arctic are changing. There is a negative trend in the surface albedo due to negative trends in snow and ice cover. The extent and duration of snow and ice cover are decreasing, and the changes are most prominent in spring due to the strong surface albedo feedback. Snow water equivalent (SWE) also shows negative trends globally, but significant spatial variability exists especially in the Arctic.
Signe Aaboe presents the data and software that OSI SAF offers to users.
Ocean and Sea Ice Satellite Application Facility (OSI SAF) was initialised in 1997 and is one of eight EUMETSAT Satellite Application Facilities, which provide users with operational data and software products from meteorological satellites.
The OSI SAF programme has a focus on the ocean-atmosphere interface and derives ocean products of wind, temperature, radiative fluxes and several perspectives of the sea ice. This presentation will give an introduction to OSI SAF, - what it is, who is involved, and which products are derived. The main focus will be on sea-ice developments and products.
Robert Hausen describes his journey in the Arctic during the MOSAIC project.
The presentation is about the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) from a forecaster’s perspective. As kind of an experience report this talk gives some impressions from the preparation before the expedition started up to the uncertain outcome of Leg 3 of 6 due to the Corona Crisis and other circumstances.
Jari Haapala explains how thermodinamical and dynamical factors affect the arctic sea ice characteristics.
The mass balance of the Arctic Sea ice depends on thermodynamical and dynamical factors. Thermodynamical and mechanical sea ice state variables are strongly coupled, but the strength of coupling varies in daily, seasonal, and climate time scales. When the ice pack is thick, solid, and compact, this coupling is strong and large areas of pack ice are mechanically connected. In these circumstances, the internal stress of pack ice accumulates and reduces differences in ice motion. In these conditions drift speed of the Arctic Sea decreases, the age of ice increases and the total mass of the ice pack increases. On the contrary, a thinner ice pack which includes cracks, leads, or larger open water areas is in turn mechanically weakly connected, exhibits larger variations in motions in shorter time and length scales drifts with higher speed, and exhibits shorter residence time in the Arctic. In this talk, the importance of ice dynamics on sea ice mass balance is reviewed and new findings based on the MOSAiC campaign are discussed.
Patrick Eriksson speaks about monitoring sea ice in the very heavily trafficked Baltic Sea
The Baltic Sea, a relatively small semi-closed basin with brackish water, is one of the most heavily trafficked sea areas in the world. Thousands of vessels visit ice-infested ports every winter, which requires well organized icebreaking. To support this winter navigation operation, the Ice Service at FMI conducts monitoring and forecasting of the sea ice throughout the winter. An essential source of information is obviously satellite data. Imagery from several satellite platforms is not only processed to serve ice charting and reporting, but is also delivered in near-real-time straight to the bridges of the icebreakers. The dark and cloudy Nordic winter has proven the Synthetic Aperture Radar instruments (SAR) to be the most suitable when analysing the development of the sea ice. Different passive instruments are also used, light and cloudiness permitting. All satellite platforms bring their various strengths and limitations on the ice analyst’s desktop, causing constantly changing challenges to the charting of sea-ice features. So far, the analysis has been predominantly manual work, but the ever-increasing data volumes are setting a demand for AI-based automatic interpretation.
Vojtěch Bližňák presents how the satellite cloud data helps forecasting road surface temperature in Czechia.
The goal of the contribution is to assess an impact of extrapolated cloud cover derived from satellite observations on forecasts of road surface temperature (RST) performed by the road weather model (RWM) FORTE. Based on road weather station measurements and forecasts of the ALADIN numerical weather prediction (NWP) model, which are used as inputs to prepare initial and boundary conditions, the RWM generates a linearly continuous forecast of RST on selected Czech highways. The work will compare the evaluation of RST forecasts generated by two model runs using NWP and satellite-derived cloud cover estimation.
Vesa Nietosvaara showcases how the MTG's FCI instrument will improve the quality of satellite data, especially for users in high latitudes.
The first Meteosat Third Generation (MTG-I) satellite with Flexible Combined Instrument (FCI) was launched at the end of 2022. It will be followed later in 2024 by MTG-S Satellite with Infrared Sounder onboard. MTG will carry novelty instruments – Infrared Sounder, Lightning Imager and Ultraviolet Visible Near-infrared (UVN) Spectrometer - in the GEO orbit. Meteosat Third Generation aims to secure continuity and to increase the capabilities of the Meteosat satellites in response to requirements of the future forecast/nowcast systems. Altogether, the new and enhanced capabilities will allow us to make a huge step in better monitoring of our environment, and allowing development of new applications.
Reima Eresmaa presents how satellite data helps in numerical weather prediction systems to support meteorological applications in Finland and surrounding countries.
MetCoOp is a group devoted to developing and operating rapidly-updating kilometric-scale Numerical Weather Prediction (NWP) systems to support meteorological applications in Finland, Scandinavia and Baltic countries. There are currently two operational NWP systems: The MetCoOp Ensemble Prediction System (MEPS) and the MetCoOp Nowcasting System (MNWC). Forecasts are provided in a limited-area grid in 2.5 km horizontal resolution and on 65 levels.
MetCoOp NWP systems assimilate a variety of satellite-based data provided by polar orbiters of NOAA, EUMETSAT, and CMA. In particular, extensive use is made of radiance measurements in microwave and infrared wavelengths. Via three-dimensional variational data assimilation, such observations help constrain the NWP model state at the initial time of each forecast. The radiance observations provide information on temperature and humidity at different altitudes.
Virve Karsisto shows the road model FMI uses to forecast road conditions.
Finnish Meteorological Institute’s (FMI) road weather model has been in operational use over 20 years. The forecasts help in the road maintenance decision making and give useful information for road users about the driving conditions. Recently, a shadowing algorithm has been implemented to the model to take in to account the shadowing caused by the surrounding objects. This helps to make more localized forecasts, as shadowing can cause great differences in road temperature even between different lanes.