How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

[1]  S. Crewell,et al.  Long-Term Observations and High-Resolution Modeling of Midlatitude Nocturnal Boundary Layer Processes Connected to Low-Level Jets , 2018 .

[2]  Benjamin M. Herman,et al.  Determination of aerosol height distributions by lidar , 1972 .

[3]  Marc-Antoine Drouin,et al.  Radiation fog formation alerts using attenuated backscatter power from automatic lidars and ceilometers , 2016 .

[4]  T. Marke,et al.  Atmospheric Boundary Layer Classification With Doppler Lidar , 2018, Journal of Geophysical Research: Atmospheres.

[5]  A. Haefele,et al.  PathfinderTURB: an automatic boundary layer algorithm. Development, validation and application to study the impact on in situ measurements at the Jungfraujoch , 2017 .

[6]  M. Piringer,et al.  Mixing-Height Time Series from Operational Ceilometer Aerosol-Layer Heights , 2016, Boundary-Layer Meteorology.

[7]  S. Kneifel,et al.  RTTOV-gb – adapting the fast radiative transfer model RTTOV for theassimilation of ground-based microwave radiometer observations , 2016 .

[8]  Gerd Teschke,et al.  Mean wind vector estimation using the velocity–azimuth display (VAD) method: an explicit algebraic solution , 2017 .

[9]  Matthias Wiegner,et al.  Aerosol profiling with the Jenoptik ceilometer CHM15kx , 2012 .

[10]  Timo Vihma,et al.  Methodology for obtaining wind gusts using Doppler lidar , 2017 .

[11]  S. Ballard,et al.  Evaluation of forward-modelled attenuated backscatter using an urban ceilometer network in London under clear-sky conditions , 2018, Atmospheric Environment.

[12]  Josef Gasteiger,et al.  Correction of water vapor absorption for aerosol remote sensing with ceilometers , 2015 .

[13]  E. O'connor,et al.  A Technique for Autocalibration of Cloud Lidar , 2004 .

[14]  J. C. Liljegren,et al.  A multichannel radiometric profiler of temperature, humidity, and cloud liquid , 2003 .

[15]  David D. Turner,et al.  The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals , 2013 .

[16]  L. Alados-Arboledas,et al.  Assimilation of humidity and temperature observations retrieved from ground‐based microwave radiometers into a convective‐scale NWP model , 2016 .

[17]  E. O'connor,et al.  Doppler lidar measurements of oriented planar ice crystals falling from supercooled and glaciated layer clouds , 2009, 0906.0701.

[18]  M. Razinger,et al.  Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation , 2009 .

[19]  Domenico Cimini,et al.  Exploiting Existing Ground-Based Remote Sensing Networks to Improve High-Resolution Weather Forecasts , 2015 .

[20]  J. Klett Lidar inversion with variable backscatter/extinction ratios. , 1985, Applied optics.

[22]  F. Angelis,et al.  Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network , 2017 .

[23]  Ville Vakkari,et al.  A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland , 2015 .

[24]  Susanne Crewell,et al.  Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment , 2014 .

[25]  R. Engelmann,et al.  An overview of the first decade of Polly NET : an emerging network of automated Raman-polarization lidars for continuous aerosol profiling , 2016 .

[26]  V. Masson,et al.  The AROME-France Convective-Scale Operational Model , 2011 .

[27]  A. Illingworth,et al.  A robust automated technique for operational calibration of ceilometers using the integrated backscatter from totally attenuating liquid clouds , 2019, Atmospheric Measurement Techniques.

[28]  Oleg A. Krasnov,et al.  Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations , 2007 .

[29]  Martial Haeffelin,et al.  Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers , 2016 .

[30]  Ina Mattis,et al.  Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements , 2018, Geoscientific Model Development.

[31]  L. Sauvage,et al.  Evaluation of Mixing-Height Retrievals from Automatic Profiling Lidars and Ceilometers in View of Future Integrated Networks in Europe , 2012, Boundary-Layer Meteorology.

[32]  Alexander Haefele,et al.  An empirical method to correct for temperature-dependent variations in theoverlap function of CHM15k ceilometers , 2016 .

[33]  Clemens Simmer,et al.  A network suitable microwave radiometer for operational monitoring of the cloudy atmosphere , 2005 .

[34]  Lucie Rottner,et al.  Stochastic method for turbulence estimation from Doppler lidar measurements , 2017 .

[35]  Victoria E. Cachorro,et al.  Near-real-time processing of a ceilometer network assisted with sun-photometer data: monitoring a dust outbreak over the Iberian Peninsula , 2017 .

[36]  Alfredo Peña,et al.  Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites , 2016, Boundary-Layer Meteorology.

[37]  V. Cachorro,et al.  Retrieval of aerosol profiles combining sunphotometer and ceilometer measurements in GRASP code , 2018 .

[38]  Domenico Cimini,et al.  Forecast indices from a ground-based microwave radiometer for operational meteorology , 2014 .

[39]  C. Grimmond,et al.  Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds , 2018, Quarterly Journal of the Royal Meteorological Society.

[40]  Domenico Cimini,et al.  Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals , 2018, Atmospheric Chemistry and Physics.

[41]  E. O'connor,et al.  Low-Level Jets over Utö, Finland, Based on Doppler Lidar Observations , 2016 .

[42]  Ronny Leinweber,et al.  An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial , 2015 .

[43]  E. O'connor,et al.  A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements , 2010 .

[44]  Diofantos G. Hadjimitsis,et al.  Low-level mixing height detection in coastal locations with a scanning Doppler lidar , 2014 .