Day–Night Monitoring of Volcanic SO2 and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO2) satellite data. We developed low-latency quantitative retrievals of SO2 column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO2 and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO2 and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO2, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide.

Autonomous driving in adverse winter weather conditions

<p>Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. </p><p>Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.</p><p>Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.</p><p>This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.</p>

Serving Open Data from mixed on-premise and cloud environment at the Finnish Meteorological Institute.

<p>Finnish Meteorological Institute has a long history of open data. Partly as a result of the INSPIRE directive almost all important data was opened back in 2013. Because of this we have quite a long history of usage of the data and as well experience on technical solutions and user needs. The presentation will open up the current status and future development keeping in mind the upcoming WMO WIS2 development as well as the Open Data directive with its High Value Dataset proposal which will very likely feature meteorological datasets.</p><p>Data is provided via machine-readable interfaces as well as human usable web interfaces. We use on-premise storage and interfaces and in addition also offer cloud-based distribution such as the Amazon Public Dataset program. The current operational interfaces are based on WFS 2.0 and WMS. Most recently added datasets include weather and flood warnings in Common Alerting Protocol (CAP) format, black carbon measurements and radar data archive via Amazon S3 in GeoTIFF and HDF5 formats. There is development starting for providing data via even more developer-friendly interfaces such as the OGC Features API. Also new data is being added continuously based on our own and user needs.</p><p>An impact study has also been conducted for the year 2018 which reveals some findings on what data is used and how it impacts the users and their potential businesses. Also valuable information on the future needs of users was gathered and the most important findings of this study will be presented during the session.</p>

Atmospheric aerosol, gases, and meteorological parameters measured during the LAPSE-RATE campaign by the Finnish Meteorological Institute and Kansas State University

Small unmanned aerial systems (sUASs) are becoming very popular as affordable and reliable observation platforms. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), conducted in the San Luis Valley (SLV) of Colorado (USA) between 14 and 20 July 2018, gathered together numerous sUASs, remote-sensing equipment, and ground-based instrumentation. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) co-operated during LAPSE-RATE to measure and investigate the properties of aerosol particles and gases at the surface and in the lower atmosphere. During LAPSE-RATE the deployed instrumentation operated reliably, resulting in an observational dataset described below in detail. Our observations included aerosol particle number concentrations and size distributions, concentrations of CO2 and water vapor, and meteorological parameters. All datasets have been uploaded to the Zenodo LAPSE-RATE community archive (https://zenodo.org/communities/lapse-rate/, last access: 21 August 2020). The dataset DOIs for FMI airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3993996, Brus et al. (2020a), and those for KSU airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3736772, Brus et al. (2020b).

An Alternative Machine Learning-Based Methodology for H-SAF H35 Fractional Snow Cover Product

<p>The fractional snow cover (FSC) product H35 is a daily operational product based on multi-channel analysis of AVHRR onboard to NOAA and MetOp satellites. H35 is supplied by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF). The “traditional” H35 FSC product is generated at pixel resolution by exploiting the brightness intensity, which is the convolution of the snow signal and the fraction of snow within the pixel and the sampling is carried out at 1-km intervals. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. This presentation aims to represent the latest findings of our efforts in developing an “alternative” H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs). In total, 332 Sentinel 2 images over Alps, Tatra Mountains and Turkey acquired between November 2018 and April 2019 are used in order to generate the necessary reference FSC maps for the training of the MARS and RF models. AVHRR bands 1-5, NDSI and NDVI are used as predictor variables. Binary classified Sentinel 2 snow maps, ERA5 snow depth and MODIS MOD10A1 NDSI data are employed in the validation of the models. The results show that both MARS- and RF-based H35 product are i) in good agreement with reference FSC maps (as indicated by low RMSE and relatively high R values) and ii) able to capture the spatial variability of the snow extend. However, MARS-based H35 is preferred for an operational FSC product generation due to the high computational cost required in RF model.</p>

Calibration and first results of relative humidity sensor MEDA HS on board M2020 rover

<p><span><span>MEDA HS is the relative humidity sensor </span></span><span><span>on the</span></span><span><span> Mars 2020 Perseverance rover provided by the Finnish Meteorological Institute (FMI). The sensor is a part of Mars Environmental Dynamic Analyzer (MEDA), a suite of environmental sensors provided by Centro de Astrobiología in Madrid, Spain. MEDA HS, along with METEO-H in ExoMars 2022 surface platform, is a successor of REMS-H on board Curiosity.</span></span></p><p><span><span>Calibration of relative humidity (RH) instruments for Mars missions is challenging due to the range of RH (from 0 to close to 100%) and temperature conditions (from about -90 ºC to + 22 ºC) that need to be simulated in the lab. Thermal gradients in different parts of the system </span></span><span><span>need to</span></span><span><span> be well known and controlled to ensure reliable reference RH readings. For MEDA HS the calibration tests have been performed for different models of MEDA HS in three Martian humidity simulator laboratories: FMI laboratory, Michigan Mars Environmental Chamber (MMEC) and DLR PASLAB (Planetary Analog Simulation Laboratory). </span></span></p><p><span><span>MEDA HS flight model was tested at FMI together with flight spare and ground reference models in low pressure dry CO</span></span><sub><span><span>2</span></span></sub><span><span> gas from +22ºC to -70ºC and in saturation conditions from -40ºC down to -70ºC. Further, the MEDA HS flight model final calibration is complemented by calibration data transferred from an identical ground reference model which has gone through rigorous testing also after the flight model delivery. During the test campaign at DLR PASLAB that started in Autumn 2020, MEDA HS has </span></span><span><span>been calibrated</span></span><span><span> over the full relative humidity scale</span></span><span><span> between -70 to -40ºC in CO</span></span><sub><span><span>2</span></span></sub><span><span> in the pressure ranges from 5.5 to 9.5 hPa, representative of Martian surface atmospheric pressure. The results can be extrapolated to higher and lower temperatures.</span></span></p><p><span>In this presentation the final flight calibration and performance of the MEDA HS will be presented together with first results expected from the surface of Mars by the Perseverance rover.</span></p>

New operational nowcasting system at Finnish Meteorological Institute

<p>Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.</p><p>The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.</p><p>FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.</p>

TROPOMI UV radiation product and recent applications

<p>Solar ultraviolet (UV) radiation has a broad range of effects concerning life on Earth. Because of its high photon energy, UV radiation influences human health, terrestrial and aquatic ecosystems, air quality, and materials in various ways.  The Sentinel 5 Precursor (S5P) mission on a sun-synchronous orbit with an ascending node equatorial crossing at 13:30, which in conjunction with a wide swath of 2600 km provides near-global daily coverage. S5P’s TROPOMI instrument measures radiation backscattered from the Earth–atmosphere system and provides observations of atmospheric composition with the best spatial resolution presently. Among other things, TROPOMI measurements are used for calculating the UV radiation reaching the Earth's surface over the sunlit part of the globe.</p><p> </p><p>This UV-radiation product is processed at the Finnish Meteorological Institute Copernicus Collaborative Ground segment. The product was released via FinHUB in summer 2020. The TROPOMI L2 UV product contains 36 UV parameters in total, including irradiances at four different wavelengths and dose rates for erythemal and vitamin D synthesis action spectra. All parameters are calculated for overpass time, for solar noon time, and for theoretical clear-sky conditions with no clouds or aerosols. Daily doses and accumulated irradiances are also calculated by integrating over the sunlit part of the day. In addition to UV parameters, quality flags related to the UV product and processing are generated.Validation with ground based instruments have shown that the agreement is very good, typically within 10%.</p><p> </p><p>The S5P is the first Copernicus mission dedicated to atmospheric observations, and it will be complemented by Sentinel 4 with geostationary orbit and Sentinel 5 on Sun-synchronous morning orbit with planned launches in the coming years.  It is expected that surface UV-radiation products from these instruments will continue the present time series.</p><p> </p><p>The TROPOMI surface UV radiation product responds to the increasing need for information regarding the tropospheric chemistry and biologically active wavelengths of the solar spectrum reaching the surface. In this presentation we introduce the TROPOMI UV radiation product and future developments, discuss about the quality of the product and demonstrate the usefulness of the satellite UV-data by showing resent applications including among others the exceptionally high UV-radiation conditions in mid latitudes due to persistent Antarctic ozone hole in December 2020 and modeling of seasonal cycle of COVID-19. By combining  the TROPOMI UV data with observations of trace gases from the same instrument, there is also a potential for new kind of applications, where satellite data can be used in novel ways to study photochemical processes in the troposphere.</p>

Tailored seasonal snow forecasts for ski centers in Finland

<p>In Finland, the ski industry is facing an increased vulnerability to climate change and variability, especially in southern and central regions. The late start and the early end of snowing season and the difficulties in artificial snow production due to high winter temperatures have significant impacts on winter tourism. As part of INDECIS project, Finnish Meteorological Institute developed and tested seasonal forecasts with Finnish ski centers, providing support in maintenance practices. In the beginning of the pilot, a workshop was organized representatives of the ski resorts, where the most useful indices were selected, uncertainties related to variables used in the development of indices were presented to the users and the visualization and delivery of climate outlooks were agreed. In this presentation, we will assess the quality of snow forecast and present the developed seasonal snow outlooks. </p><p>The ECMWF long-range forecasts (SEAS5) were quality assessed and several bias-adjusted methods analysed. Finally, the raw snow forecast was bias-adjusted using the EMOS method. The forecasts were the monthly mean snow depth, and the probability of ≥1 cm of monthly mean snow depth. The forecasts were evaluated using the CRPSS. The results depend much on the season. For example, Lead month 0 and month 1 forecasts in February showed skill over most of Finland, while Lead month 0 and month 1 forecasts in November were not as skilful. </p><p>The developed seasonal climate outlooks were tested by the users during November 2019-April 2020; following the test period a feedback survey was conducted with the users. How the perceived usefulness of forecasts transfers to the decisions made by the users is not so straight-forward. According to the feedback received only one user of the four repliers changed their plans based on the provided outlooks, and half of the respondents couldn't say if they changed their activities in any way.</p>

Measurement report: Properties of aerosol and gases in the vertical profile during the LAPSE-RATE campaign

Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.