Cloud Observations at UFS Schneefernerhaus Towards the Evaluation of Satellite Observations and Numerical Weather Prediction

Transcription

1 Cloud Observations at UFS Schneefernerhaus Towards the Evaluation of Satellite Observations and Numerical Weather Prediction Martin Hagen 1, Tobias Zinner 2, Bernhard Mayer 2, Axel Häring 1,2 1 Institut für Physik der Atmospäre, DLR Oberpfaffenhofen 2 Meteorologisches Institut, LMU München

2 Motivation Various instruments at UFS Schneefernerhaus observe properties of clouds and precipitation Various satellites (geostationary and orbiting) retrieve properties of clouds and precipitation Various numerical weather prediction models forecast properties of clouds and precipitation Satellite retrieval algorithms call for evaluation and improvement of the retrieval algorithms Numerical weather prediction lacks accurate prediction of clouds and precipitation and needs refinement of the microphysics parameterization Measurements at UFS Schneefernerhaus provide the opportunity for the evaluation of satellite products and numerical weather prediction VAO Symposium, Grenoble, March 2018, Martin Hagen 2

3 Cloud/Precipitation related Instruments at UFS Cloud radar MIRA35 (vertical) (operated by DLR-IPA) Ka-band, 35 GHz - backscatter profiles - vertical velocities - depolarization Ceilometer CMK15k (operated by DWD) - backscatter profiles - cloud base Microwave radiometer HATPRO and DPR (operated by U. Köln) - temperature profiles - humidity profiles - liquid water path LWP - ice water path IWP VAO Symposium, Grenoble, March 2018, Martin Hagen 3

4 Further Cloud/Precipitation related Instruments at UFS 2D-Video Disdrometer (Helmholtz-Z. Mün.) with Pluvio rain gauge (U. Köln) - particle size distribution - particle shape and mass (with Pluvio) Micro-Rain Radar MRR (DLR-IPA) - backscatter profiles - vertical velocity Parsivel-2 (DLR-IPA) - particle size distribution Raman Lidar (KIT) water vapor DIAL (KIT) further Lidar s also on summit (KIT) VAO Symposium, Grenoble, March 2018, Martin Hagen 4

5 Some Cloud Statistics from Cloud Radar more than half of the year (2014 data) the sky above Schneefernerhaus is covered by clouds three distinct local reflectivity maxima cirrus clouds low clouds precipitation no signal first m above radar VAO Symposium, Grenoble, March 2018, Martin Hagen 5

6 Cloudnet for Evaluation of Numerical Models VAO Symposium, Grenoble, March 2018, Martin Hagen 6

7 UFS Schneefernerhaus Ready for Cloudnet Supersite Scope: Long-term observations of cloud parameters for the systematically comparison to forecast and climate models: To optimize the use of data sets to develop and validate cloud remote sensing synergy algorithms To continuously evaluate the representation of clouds in climate and weather forecast models To demonstrate the potential of an operational network Illingworth et al., 2007, BAMS, Cloudnet is part of the H2020 project ACTRIS (April March 2019) Represented by HD(CP) 2 project in Germany VAO Symposium, Grenoble, March 2018, Martin Hagen 7

8 UFS Schneefernerhaus Ready for Cloudnet Supersite Instruments: cloud radar ceilometer/lidar microwave radiometer rain gauge Variables: cloud fraction LWC and IWC plus a number of others Sites: 10+ across Europe plus world-wide ARM sites 8+ models: global, mesoscale and high-resolution forecast models in operation since 2003 Summit Mace Head Cabauw Chilbolton Jülich Palaiseau Graciosa AMF COPS Barbados Ny Ålesund Sodankylä AMF Hyytiälä Hyytiälä Lindenberg Leipzig München (MIM) Schneefernerhaus Potenza Cyprus Cloudnet Sites ARM/NOAA Sites AMF Sites VAO Symposium, Grenoble, March 2018, Martin Hagen 8

9 Cloudnet Products Target classification Cloud properties IWC, LWC, LWP Drizzle properties Future: Aerosol properties (EARLINET processing) Illingworth et al., 2007 BAMS, 88, VAO Symposium, Grenoble, March 2018, Martin Hagen 9

10 Cloudnet Products Observations at Schneefernerhaus Radar reflectivity factor Attenuated backscatter factor 8 Jan elevation of Schneefernerhaus elevation of Schneefernerhaus Radar and lidar detection status elevation of Schneefernerhaus Ice water content elevation of Schneefernerhaus VAO Symposium, Grenoble, March 2018, Martin Hagen 10

11 Cloudnet Products Model (DWD ICON global) Relative humidity 8 Jan Ice water mixing ratio Cloud fraction VAO Symposium, Grenoble, March 2018, Martin Hagen 11

12 Cloudnet towards Evaluation of Satellite Measurements VAO Symposium, Grenoble, March 2018, Martin Hagen 12

13 Satellite observations over UFS Schneefernerhaus Polar orbiting satellites (with cloud radar and/or lidar): Cloudsat, Calipso, GPM, EarthCARE high spatial resolution (approx. 500 m) vertical profiles pass twice a day only every 5 to 15 days (almost) direct overpass Geostationary satellites: MSG, MTG high temporal resolution (5-15 minutes) coarse spatial resolution cloud top observations only cloud penetration depth depending on sensor long term statistics required for evaluation / validation VAO Symposium, Grenoble, March 2018, Martin Hagen 13

14 UFS Cloud Radar and Cloudsat Radar Cloudsat cloud radar W-band 95 GHz statistics overpass within 15km follow up satellite: EarthCARE Häring, 2016 VAO Symposium, Grenoble, March 2018, Martin Hagen 14

15 UFS Cloud Radar and SEVIRI on Board of MSG Ice water path IWP integrated from UFS cloud radar reflectivity (Z) fall velocity (Z-v t ) temperature (Z-T) (Matrosov et al., 2002; Protat et al., 2007) height AGL (km) : :00 Ice water path IWP derived from MSG SEVIRI (Bugliaro et al., 2011) height AGL (km) Häring, 2016 VAO Symposium, Grenoble, March 2018, Martin Hagen 15

16 Towards Evaluation of Satellite Observations From case studies to long-term evaluation: continuous observations by geostationary satellites compensate spatial variability within satellite pixel by temporal variability provide integrated properties and cloud top properties snapshots by orbiting satellites radar/lidar provides vertical profiles provide high resolution spatial variability satellite data relate vertical measurements above UFS to horizontal domain VAO Symposium, Grenoble, March 2018, Martin Hagen 16

17 Conclusion Outlook Cloudnet provides algorithms for synergetic observations of cloud properties Validation of numerical weather forecast models towards a better parameterization and representation of cloud and precipitation microphysics Challenges are the high spatial variability of the terrain having an influence on the spatial variability of clouds which are not resolved by most operational numerical weather forecast models which limits the representativeness of average values observed by geostationary satellites or orbiting satellites passing at a distance of several kilometers Cloudnet can serve as the basis for a long term satellite observation evaluation VAO Symposium, Grenoble, March 2018, Martin Hagen 17