The Royal Netherlands Meteorological Institute is the Dutch national weather forecasting service, which has its headquarters in De Bilt, in the province of Utrecht, Netherlands.The primary tasks of KNMI are weather forecasting, monitoring of climate changes and monitoring seismic activity. KNMI is also the national research and information centre for climate, climate change and seismology. Wikipedia.
News Article | April 17, 2017
Meteorologists have long struggled to forecast storms and flooding at the level of streets and neighborhoods, but they may soon make headway thanks to the spread of mobile-phone networks. This strategy relies on the physics of how water scatters and absorbs microwaves. In 2006, researchers demonstrated that they could estimate how much precipitation was falling in an area by comparing changes in the signal strength between communication towers1. Accessing the commercial signals of mobile-phone companies was a major stumbling block for researchers, however, and the field progressed slowly. That is changing now, enabling experiments across Europe and Africa. The technology now appears ready for primetime. It could lead to more precise flood warnings — and more accurate storm predictions if the new data are integrated into modern weather forecasting models. Proponents also hope to use this approach to expand modern weather services in developing countries. The newest entry into this field is ClimaCell, a start-up company in Boston, Massachusetts, that launched on 2 April. The 12-person firm says that it can integrate data from microwave signals and other weather observations to create more accurate short-term forecasts. It notes it can provide high-resolution, street-level weather forecasts three hours ahead, and will aim to provide a six-hour forecast within six months. The company has yet to make information on its system public or publish it in peer-reviewed journals. ClimaCell will start in the United States and other developed countries, but plans to move into developing countries including India later this year. “The signals are everywhere, so basically we want to cover the world,” says Shimon Elkabetz, ClimaCell’s chief executive and co-founder. But the fledgling company faces competition from researchers in Europe and Israel who have tested systems at multiple scales, including countries and cities, over the past several years. The scientists recently formed a consortium to advance the technology using open-source software. Coordinated by Aart Overeem, a hydrometeorologist at the Royal Netherlands Meteorological Institute in De Bilt, the group is seeking nearly €5 million (US$5.3 million) from the European Commission to create a prototype rainfall-monitoring system that could eventually be set up across Europe and Africa. “There is a lot of evidence that this technology works, but we still need to test it in more regions with large data sets and different networks,” Overeem says. Although ClimaCell has made bold claims about its programme, Overeem says he cannot properly review the company's technology without access to more data. “The fact that a start-up company and commercial investors are willing to put money into this technology is good news, but I believe there is room for all,” says Hagit Messer, an electrical engineer at Tel Aviv University in Israel, who led the 2006 study. She is part of the research consortium led by Overeem. Previous projects by members of the consortium that tested the technology have met with success. In 2012, for instance, Overeem and his colleagues showed that the technology could be applied at the country level using commercial microwave data in the Netherlands2. And in 2015, the Swedish Meteorological and Hydrological Institute (SMHI), headquartered in Norrköping, launched a prototype real-time ‘microweather’ project in Gothenburg. It collects around 6 million measurements in the city each day in partnership with the telecommunications company Ericsson and a cellular tower operator. The result is a minute-by-minute estimate of rainfall on a 500-metre-resolution map that encompasses the city. Jafet Andersson, an SMHI hydrologist, says that the project has helped to advance the technology. For example, he notes that microwave data often overestimate rainfall by as much as 200–300%. But the team has worked out how to correct for that bias without relying on reference measurements from rain gauges or ground-based radar. This will make it easier to extend the technology to developing countries. “It will take some time, but we are in the process of industrializing it on a country scale, or even a global scale,” Andersson says. Researchers with the consortium have deployed the technique in African countries that do not have access to ground-based radar and extensive rain-gauge networks. A team led by Marielle Gosset, a hydrologist at the French Institute for Development Research in Toulouse, demonstrated a proof-of-concept system in Burkina Faso3 in 2012 and has since branched out to other countries including Niger. Working with French telecoms giant Orange, and with funding from the World Bank and the United Nations, her team hopes to expand into Morocco and begin using real-time microwave data in Cameroon this year. The technology is attracting interest in Africa because conventional weather-monitoring systems such as radar are too expensive, Gosset says. Weather forecasts based on microwave signals give developing countries a similar system, but for less money, she says. Access to commercial data is getting easier, too. Researchers say that telecommunication companies are beginning to see the value of releasing the data, and the consortium plans to create a central repository for processing the information. Project scientists hope to create a model that will enable a smooth partnership with the industry. “I think that this door is just about to open,” says Andersson.
News Article | June 22, 2017
Water expertise and machine learning prove the winning combination for Smart water management. The competition is an initiative set up by a number of partners in the "Smart water management" programme, which includes Rijkswaterstaat and several regional public water authorities. Participants in the challenge were asked to develop a method to extract as much social, ecological or economic efficiency from a cubic meter of water. The data was derived from the catchment and drainage area of the North Sea Canal and Amsterdam-Rhine Canal. Due to the complexity of the involved water systems, the data science specialists of HAL24K and Tauw’s water experts decided to collaborate. The winning solution combined artificial intelligence (AI) and machine learning (ML). Employing advanced methods of data intelligence, HAL24K and Tauw modeled the operational (flexible) water level management on a large-scale and with a high degree of accuracy. By using historical data in conjunction with ML and AI, the team predicted water levels and what adjustments would be required for optimal energy consumption and various climate conditions. The solution also highlighted how to ensure optimal water distribution between the different management areas. Jerome Mol, CEO of HAL24K said: “We have developed algorithms that can learn from past data to provide real-time predictions. It is good to see that even in water management in the Netherlands, which is already administered very well, our advanced data science can make a positive contribution. Ultra-complex systems such as these suit our HAL24K Platform perfectly.” HAL24K and Tauw investigated the water damage in the management area of Hoogheemraadschap de Stichtse Rijnlanden. They used precipitation data, pump data, target levels and surface water levels over the past five years to build the AI and ML models. The jury was impressed with the comprehensive solution and the visualization of the model and how it identified underlying issues. The HAL24K and Tauw team was able to deliver a concrete result in a short period of time, which is applicable to multiple management areas and lends itself perfectly for further development. Annemieke Nijhof, CEO of Tauw Group said: “The results show the enormous potential of machine learning and artificial intelligence. They provide an excellent basis for many applications within water management. This enables us to better support decision making by administrators and policy makers.” Notes for editors: About the data challenge The Data Challenge 2017 took place within the framework of the "Smart water management" programme. The organization included Rijkswaterstaat, Water Authority Amstel, Gooi & Vecht, Hoogheemraadschap de Stichtse Rijnlanden, Hoogheemraadschap van Rijnland, Hoogheemraadschap Hollands Noorderkwartier, in cooperation with Statistics Netherlands (CBS), the Royal Netherlands Meteorological Institute (KNMI), STOWA, Dutch Water Authorities and Nelen & Schuurmans. Rijkswaterstaat acted as secretary. About Tauw Tauw Group is an international firm of consulting engineers with branches in the Netherlands, Belgium, Germany, France, Spain and Italy with over 1,000 employees. In the Netherlands the organization operates as Tauw bv and Atrivé bv. Using the theme "Tauw Takes Care" the company supports clients in a responsible way with clear recommendations taking into account all aspects in the field of the environment, safety, energy, water and the living environment. www.tauw.com About HAL24K HAL24K is a Data Intelligence Lab based in San Francisco, Amsterdam and London, delivering operational and predictive intelligence to Smart cities and Smart enterprises. It combines advanced data science techniques – such as machine learning and deep neural networks – with modelling, analysis and visualization through its SaaS-based HAL24K Dimension platform, to enable real-time data-driven decision making in complex and multidimensional environments. This optimizes resources, avoids disruptions and saves costs. www.hal24k.com Contact: Good With Words Vanessa Howard firstname.lastname@example.org Tel: +44 203 302 6701 Amsterdam, Netherlands, June 22, 2017 --( PR.com )-- Data science startup HAL24K and European consulting and engineering firm Tauw have won the Data Challenge 2017. The challenge was based around "Smart water management" and was issued by Rijkswaterstaat, the Dutch agency of the Ministry of Infrastructure and the Environment.The competition is an initiative set up by a number of partners in the "Smart water management" programme, which includes Rijkswaterstaat and several regional public water authorities. Participants in the challenge were asked to develop a method to extract as much social, ecological or economic efficiency from a cubic meter of water.The data was derived from the catchment and drainage area of the North Sea Canal and Amsterdam-Rhine Canal. Due to the complexity of the involved water systems, the data science specialists of HAL24K and Tauw’s water experts decided to collaborate.The winning solution combined artificial intelligence (AI) and machine learning (ML). Employing advanced methods of data intelligence, HAL24K and Tauw modeled the operational (flexible) water level management on a large-scale and with a high degree of accuracy. By using historical data in conjunction with ML and AI, the team predicted water levels and what adjustments would be required for optimal energy consumption and various climate conditions. The solution also highlighted how to ensure optimal water distribution between the different management areas.Jerome Mol, CEO of HAL24K said: “We have developed algorithms that can learn from past data to provide real-time predictions. It is good to see that even in water management in the Netherlands, which is already administered very well, our advanced data science can make a positive contribution. Ultra-complex systems such as these suit our HAL24K Platform perfectly.”HAL24K and Tauw investigated the water damage in the management area of Hoogheemraadschap de Stichtse Rijnlanden. They used precipitation data, pump data, target levels and surface water levels over the past five years to build the AI and ML models. The jury was impressed with the comprehensive solution and the visualization of the model and how it identified underlying issues. The HAL24K and Tauw team was able to deliver a concrete result in a short period of time, which is applicable to multiple management areas and lends itself perfectly for further development.Annemieke Nijhof, CEO of Tauw Group said: “The results show the enormous potential of machine learning and artificial intelligence. They provide an excellent basis for many applications within water management. This enables us to better support decision making by administrators and policy makers.”Notes for editors:About the data challengeThe Data Challenge 2017 took place within the framework of the "Smart water management" programme. The organization included Rijkswaterstaat, Water Authority Amstel, Gooi & Vecht, Hoogheemraadschap de Stichtse Rijnlanden, Hoogheemraadschap van Rijnland, Hoogheemraadschap Hollands Noorderkwartier, in cooperation with Statistics Netherlands (CBS), the Royal Netherlands Meteorological Institute (KNMI), STOWA, Dutch Water Authorities and Nelen & Schuurmans. Rijkswaterstaat acted as secretary.About TauwTauw Group is an international firm of consulting engineers with branches in the Netherlands, Belgium, Germany, France, Spain and Italy with over 1,000 employees. In the Netherlands the organization operates as Tauw bv and Atrivé bv. Using the theme "Tauw Takes Care" the company supports clients in a responsible way with clear recommendations taking into account all aspects in the field of the environment, safety, energy, water and the living environment.www.tauw.comAbout HAL24KHAL24K is a Data Intelligence Lab based in San Francisco, Amsterdam and London, delivering operational and predictive intelligence to Smart cities and Smart enterprises.It combines advanced data science techniques – such as machine learning and deep neural networks – with modelling, analysis and visualization through its SaaS-based HAL24K Dimension platform, to enable real-time data-driven decision making in complex and multidimensional environments. This optimizes resources, avoids disruptions and saves costs.www.hal24k.comContact:Good With WordsVanessa Howardvanessa@goodwithwords.bizTel: +44 203 302 6701 Click here to view the list of recent Press Releases from Interchange Group
News Article | June 29, 2017
The June heat waves that impacted much of the UK and Western Europe were made more intense because of climate change say scientists. Forest fires in Portugal claimed scores of lives while emergency heat plans were triggered in France, Switzerland and the Netherlands. Britain experienced its warmest June day since the famous heat wave of 1976. Human-related warming made record heat 10 times more likely in parts of Europe the researchers say. During June, mean monthly temperatures about 3C above normal were recorded across western parts of the continent. France experienced its hottest June night ever on 21st when the average around the country was 26.4C. That same day had seen the mercury hit 34.5 at Heathrow in what was the UK's warmest June day for 40 years. It was a similar story in the Netherlands which is set to have its hottest June on record while in Switzerland it was the second warmest since 1864. Now, researchers with World Weather Attribution have carried out a multi-method analysis to assess the role of warming connected to human activities in these record temperatures. "We simulate what is the possible weather under the current climate and then we simulate what is the possible weather without anthropogenic climate change, and then we compare these two likelihoods which gives us the risk ratio," Dr Friederike Otto from the University of Oxford, one of the study's authors, told BBC News. That signal, according to the authors, made heat waves at least 10 times more likely in Spain and Portugal. Fires resulted in the deaths of 64 people in Portugal, while in Spain they forced the removal of around 1,500 people from holiday accommodation and homes. In Central England, France, Switzerland and the Netherlands the intensity and frequency of such extreme heat was four times as likely because of climate change, the study says. "We found clear and strong links between this month's record warmth and human-caused climate change," said Geert Jan van Oldenborgh, senior researcher at the Royal Netherlands Meteorological Institute (KNMI). "Local temperature records show a clear warming trend, even faster than in climate models that simulate the effects of burning fossil fuels but also solar variability and land use changes," van Oldenborgh added. The researchers say their reported results on the impact made by human related warming are conservative in some ways. Their study indicated that in countries like Spain, Portugal and France, climate change could be increasing the chances of extreme heat by up to forty times. The scientists believe that the chances of these extreme heat events becoming much more common will increase unless rapid steps are taken to reduce carbon emissions. "Hot months are no longer rare in our current climate. Today we can expect the kind of extreme heat that we saw in June roughly every 10 to 30 years, depending on the country," said Robert Vautard, a researcher at the Laboratory of Climate and Environmental Sciences (LSCE), who was also involved in the study. "By the middle of the century, this kind of extreme heat in June will become the norm in Western Europe unless we take immediate steps to reduce greenhouse gas emissions." The researchers are calling on city leaders in particular to work with scientists and public health experts to develop heat action plans. While, usually, researchers wait to publish research like this in a peer-reviewed journal, the team felt that speed was necessary to inform public debate. "When extreme events happen, the question is always asked 'what's the role of climate change?' and often the statement is made by a politician or by someone with a political agenda and not based on scientific evidence," said Dr Otto. "Our aim is to provide that for the role of climate change, to show what you can robustly say within the time frame when people are discussing the event." Follow Matt on Twitter and on Facebook
Bintanja R.,Royal Netherlands Meteorological Institute |
Van Oldenborgh G.J.,Royal Netherlands Meteorological Institute |
Drijfhout S.S.,Royal Netherlands Meteorological Institute |
Wouters B.,Royal Netherlands Meteorological Institute |
Katsman C.A.,Royal Netherlands Meteorological Institute
Nature Geoscience | Year: 2013
Changes in sea ice significantly modulate climate change because of its high reflective and strong insulating nature. In contrast to Arctic sea ice, sea ice surrounding Antarctica has expanded, with record extent in 2010. This ice expansion has previously been attributed to dynamical atmospheric changes that induce atmospheric cooling. Here we show that accelerated basal melting of Antarctic ice shelves is likely to have contributed significantly to sea-ice expansion. Specifically, we present observations indicating that melt water from Antarctica's ice shelves accumulates in a cool and fresh surface layer that shields the surface ocean from the warmer deeper waters that are melting the ice shelves. Simulating these processes in a coupled climate model we find that cool and fresh surface water from ice-shelf melt indeed leads to expanding sea ice in austral autumn and winter. This powerful negative feedback counteracts Southern Hemispheric atmospheric warming. Although changes in atmospheric dynamics most likely govern regional sea-ice trends, our analyses indicate that the overall sea-ice trend is dominated by increased ice-shelf melt. We suggest that cool sea surface temperatures around Antarctica could offset projected snowfall increases in Antarctica, with implications for estimates of future sea-level rise.
De Haan S.,Royal Netherlands Meteorological Institute
Journal of Geophysical Research: Atmospheres | Year: 2011
Wind, temperature, and humidity observations from radiosonde and aircraft are the main sources of upper air information for meteorology. For mesoscale meteorology, the horizontal coverage of radiosondes is too sparse. Aircraft observations through Aircraft Meteorological Data Relay (AMDAR) sample an atmospheric profile in the vicinity of airports. However, not all aircraft are equipped with AMDAR or have the system activated. Observations inferred from an enhanced tracking and ranging (TAR) air traffic control radar can fill this gap. These radars follows all aircraft in the airspace visible to the radar for air traffic management. The TAR radar at Schiphol airport in Netherlands has a range of 270 km. This Mode-S radar contacts each aircraft every 4 s on which the transponder in the aircraft responds with a message that contains information on flight level, direction, and speed. Combined with the ground track of an aircraft, meteorological information on temperature and wind can be inferred from this information. Because all aircraft are required to respond to the TAR radar, the data volume is extremely large, being around 1.5 million observations per day. Note that there are no extra costs for this data link. The quality of these observations is assessed by comparison to numerical weather prediction (NWP) model information, AMDAR observations, and radiosonde observations. A preprocessing step is applied to enhance the quality of wind and temperature observations, albeit with a reduced time frequency of one observation of horizontal wind vector and temperature per aircraft per minute. Nevertheless, the number of observations per day is still very large. In this paper it is shown that temperature observations from Mode-S, even after corrections, are not very good; an RMS which is twice as large as AMDAR is observed when compared to NWP. In contrast to the temperature observations, the quality found for wind after correction and calibration is good; it is comparable to AMDAR, slightly worse than radiosonde but certainly very valuable for mesoscale NWP. Copyright 2011 by the American Geophysical Union.
Schneider P.,Norwegian Institute For Air Research |
Van Der A R.J.,Royal Netherlands Meteorological Institute
Journal of Geophysical Research: Atmospheres | Year: 2012
A global nine-year archive of monthly tropospheric NO2 data acquired by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument was analyzed with respect to trends between August 2002 and August 2011. In the past, similar studies relied on combining data from multiple sensors; however, the length of the SCIAMACHY data set now for the first time allows utilization of a consistent time series from just a single sensor for mapping NO2 trends at comparatively high horizontal resolution (0.25). This study provides an updated analysis of global patterns in NO2 trends and finds that previously reported decreases in tropospheric NO2 over Europe and the United States as well as strong increases over China and several megacities in Asia have continued in recent years. Positive trends of up to 4.05 (0.41) × 1015 molecules cm-2 yr-1 and up to 19.7 (1.9) % yr-1 were found over China, with the regional mean trend being 7.3 (3.1) % yr -1. The megacity with the most rapid relative increase was found to be Dhaka in Bangladesh. Subsequently focusing on Europe, the study further analyzes trends by country and finds significantly decreasing trends for seven countries ranging from -3.0 (1.6) % yr-1 to -4.5 (2.3) % yr -1. A comparison of the satellite data with station data indicates that the trends derived from both sources show substantial differences on the station scale, i.e., when comparing a station trend directly with the equivalent satellite-derived trend at the same location, but provide quite similar large-scale spatial patterns. Finally, the SCIAMACHY-derived NO2 trends are compared with equivalent trends in NO2 concentration computed using the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP) model. The results show that the spatial patterns in trends computed from both data sources mostly agree in Central and Western Europe, whereas substantial differences are found in Eastern Europe.
Van der Veen S.H.,Royal Netherlands Meteorological Institute
Monthly Weather Review | Year: 2013
The cloud mask of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is a nowcasting Satellite Application Facility (SAF) that is used to improve initial cloudiness in the High-Resolution Limited-Area Model (HIRLAM). This cloud mask is based on images from the Meteorological Satellite (Meteosat) Second Generation (MSG) satellite. The quality of the SAF cloud mask appeared to be better than initial HIRLAM clouds in 84% of the cases. Forecasts have been performed for about a week in each of the four seasons during 2009 and 2010. Better initial clouds in HIRLAMalways lead to better cloud predictions. Verification of forecasts showed that the positive impact is still present after 24 h in 59% of the cases. This is remarkable, because initial dynamics was kept unchanged. The magnitude of the positive impact on cloud predictions is more or less proportional to the initial cloud improvement, and it decreases with forecast length. Also, forecast 2-m temperatures are affected by initial clouds. The generally positive bias of the 2-m temperature errors becomes a few tenths of a degree larger during the night but it decreases a comparable amount during daylight, because MSG tends to increase the cloud amounts in HIRLAM. The standard deviation of the errors often improves slightly in the first part of the forecast, indicating that forecast temperatures correlate better with observations whenMSGis used for initialization. For longer lead times, however, standard deviations deteriorate a few tenths of a degree in seven of the eight verification periods, which all had a length of about a week. © 2013 American Meteorological Society.
Drijfhout S.S.,Royal Netherlands Meteorological Institute
Journal of Climate | Year: 2010
The response of the tropical atmosphere to a collapse of the thermohaline circulation (THC) is investigated by comparing two 5-member ensemble runs with a coupled climate model (CCM), the difference being that in one ensemble a hosing experiment was performed. An extension of the Held-Hou-Lindzen model for the Hadley circulation is developed to interpret the results. The forcing associated with a THC collapse is qualitatively similar to, but smaller in amplitude than, the solstitial shift from boreal summer to winter. This forcing results from reduced ocean heat transport creating an anomalous cross-equatorial SST gradient. The small amplitude of the forcing makes it possible to arrive at analytical expressions using standard perturbation theory. The theory predicts the latitudinal shift between the Northern Hemisphere (NH) and Southern Hemisphere (SH) Hadley cells, and the relative strength of the anomalous cross-equatorial Hadley cell compared to the solstitial cell. The poleward extent of the Hadley cells is controlled by other physics. In the NH the Hadley cell contracts, while zonal velocities increase and the subtropical jet shifts equatorward, whereas in the SH cell the opposite occurs. This behavior can be explained by assuming that the poleward extent of the Hadley cell is determined by baroclinic instability: it scales with the inverse of the isentropic slopes. Both theory and CCM results indicate that a THC collapse and changes in tropical circulation do not act in competition, as a possible explanation for abrupt climate change; they act in concert. © 2010 American Meteorological Society.
De Haan S.,Royal Netherlands Meteorological Institute
Quarterly Journal of the Royal Meteorological Society | Year: 2013
Wind, humidity and temperature observations from aircraft and radiosondes are generally used to find the best initial state of the atmosphere for numerical weather prediction (NWP). To be of use for very-short-range numerical weather forecasting (or numerical nowcasting), these observations need to be available within several minutes after observation time. Radiosondes have a typically observation latency of over 30 min and arrive too late for numerical nowcasting. Zenith Total Delay (ZTD) observations obtained from a ground-based network of Global Navigation Satellite System (GNSS) receivers can fill this gap of lacking rapid humidity information. ZTD contains information on the total amount of water vapour. Other rapidly available observations, such as radial wind estimates from Doppler weather radars, can also be exploited. Both observations are available with a delay of less than 5 min with adequate spatial resolution. In this article, the impact of assimilation of these humidity and wind observations in a very-short-range regional forecast model is assessed over a four-month summer period and a six-week winter period. As a reference for the impact, GNSS observations are also assimilated in a three-hourly NWP scheme with longer observation cut-off times. The quality of the forecasts is evaluated against radiosonde observations, radar radial wind and hourly precipitation observations. Assimilation of both GNSS ZTD and radar radial winds resulted in a positive impact on humidity, rainfall and wind forecasts. © 2013 Royal Meteorological Society.
News Article | August 22, 2016
Detecting turbulence remains the Achilles' heel of modern-day aviation. The reports submitted by pilots, subjective and often very inaccurate, are the least expensive and the most frequently used method for trying to predict where it will occur. Scientists from the Faculty of Physics, University of Warsaw, have demonstrated that turbulence can be detected in a much faster and more precise way, using data already routinely broadcast by the aircraft operated by commercial airlines. Anyone who has experienced turbulence on an airplane certainly knows that it's no fun ride. Despite advancements in technology, methods used to detect these dangerous atmospheric phenomena are still far from perfect. However, there is every indication that data allowing pilots to avoid turbulence and even to forecast such occurrences are already being routinely recorded. In fact, this has been done for many years! Jacek Kopec, a doctoral student at the Faculty of Physics, University of Warsaw, and a member of the staff of the University's Interdisciplinary Center for Mathematical and Computational Modelling (ICM), has managed to extract this valuable information from the flight parameters routinely broadcast by the transponders installed in most of the modern commercial aircraft. This new method for detecting turbulence is so original and potentially easy to implement on a large scale that the article describing it has been featured in the "highlight articles" section of the journal Atmospheric Measurement Techniques. "Today's commercial aircraft fly at altitudes of 10 to 15 km, where the temperatures fall to -60 °C. Conditions for measuring atmospheric parameters are very difficult, which explains why such measurements are not taken systematically or extensively. A lack of sufficiently accurate and up-to-date information not only exposes aircraft and their passengers to danger, it also restricts the development of theories and tools for forecasting turbulence," Jacek Kopec says. At present, pilot reports (PIREPs), relayed by radio and provided to pilots of other aircraft by air traffic controllers, are a basic source of turbulence data. Since these reports are based on the subjective opinions of pilots, the data collected in this way are often marred by substantial inaccuracies as to both the area of turbulence and its intensity. More accurate readings are provided by aircrafts involved in the Aircraft Meteorological Data Relay (AMDAR) program. This method is nonetheless costly, so data collected at cruising altitudes are transmitted relatively rarely. In practice, this prevents such reports from being used to detect and forecast turbulence. Passenger aircraft are fitted with sensors that record a variety of flight parameters. Unfortunately, most of the data are not made publicly available. Publicly available reports include only the most basic parameters such as the position of the aircraft (ADS-B transmissions, which are also used by the popular website FlightRadar24) or its speed relative to the ground and the air (Mode-S data). Meanwhile, detecting turbulence requires knowledge of the vertical acceleration of aircraft. "Vertical accelerations are especially strongly felt both by the passengers and by the aircraft," Jacek Kopec explains. "Unfortunately, there is no access to materials regarding vertical accelerations. That was why we decided to check if we could extract such data from other flight parameters, accessible in Mode-S and ADS-B transmissions. The research aircraft in a project in which I participated was fitted with a suitable transponder, so we took advantage of that fact. By coincidence, our coauthor, Siebren de Haan from the Royal Netherlands Meteorological Institute, recorded the transmissions received from the transponder," he adds. Scientists from the Faculty of Physics tested three algorithms of turbulence detection. The first relied on information about the position of aircraft (ADS-B transmissions). However, preliminary tests and their comparison against the parameters registered in the same area by the research aircraft failed to produce satisfactory results. As for the remaining two algorithms, each of them used, though in somewhat different ways, the parameters received approximately every four seconds through Mode-S transmissions. In the second approach, the parameters were analyzed using the standard theory of turbulence. In the third approach, the scientists adapted a method for determining turbulence intensity previously used to measure turbulence on a very small scale in the understory of forests. It turned out that once wind velocity in the vicinity of the aircraft was determined and its changes were analyzed in successive readings, it was possible to use the latter two theoretical approaches to locate turbulence areas with an error of only 20 km. Passenger aircraft need around 100 seconds to travel this distance, so this level of accuracy would allow pilots to maneuver their aircraft to effectively avoid turbulence. By harnessing existing data, this system of turbulence detection developed at the Institute of Geophysics (Faculty of Physics, University of Warsaw) therefore requires no significant investments in aviation infrastructure. In order to be operational, the system needs adequate software and a computer connected in a simple way to the devices that receive Mode-S transmissions from the transponders on board aircraft. Such devices are standard equipment in air traffic control institutions in Europe. In this system, passenger aircraft act as sensors by creating a dense network of measurement points above Europe. "In the coming months, we will be working to improve the software. Nevertheless, we have already achieved our most important goal: we have proved that the method for detecting turbulence we have proposed really works and can provide pilots with information enabling them to avoid dangerous areas in the atmosphere. Turbulence detection will also help improve aviation forecasting methods," stresses Prof. Szymon Malinowski from the Faculty of Physics, Jacek Kopec's doctoral dissertation advisor and one of the authors of the publication. The turbulence detection system has been developed under a grant from Poland's National Science Center (NCN). Data for the research was collected in a flight test campaign financed from the Seventh Framework Programme of the European Union.