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De Bilt, Netherlands

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.


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. Source


Trouet V.,University of Arizona | Van Oldenborgh G.J.,Royal Netherlands Meteorological Institute
Tree-Ring Research | Year: 2013

Climate Explorer (www.climexp.knmi.nl) is a web-based application for climatic research that is managed by the Royal Netherlands Meteorological Institute (KNMI) and contains a comprehensive collection of climatic data sets and analysis tools. One of its fields of application is high-resolution paleoclimatology. We show how Climate Explorer can be used to explore and download available instrumental climate data and derived time series, to examine the climatic signal in uploaded high-resolution paleoclimate time series, and to investigate the temporal and spatial characteristics of climate reconstructions. We further demonstrate the value of Climate Explorer for high-resolution paleoclimate research using a dendroclimatic data set from the High Atlas Mountains in Morocco. © 2013 The Tree-Ring Society. Source


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. Source


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. Source


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. Source

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