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ASII-NG: Developments and outlook NWCSAF 2015 Users Workshop.

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Presentation on theme: "ASII-NG: Developments and outlook NWCSAF 2015 Users Workshop."— Presentation transcript:

1 ASII-NG: Developments and outlook NWCSAF 2015 Users Workshop

2 About ASII-NG Folie 2 ASII-NG development started with CDOP-2 The first version will focus on the automatic detection of turbulence related features in satellite imagery: Lee waves Jet cloud fibers Tropopause folding Logistic regression is applied to assemble input data into a probabilistic output. Proposal to widen the scope of ASII-NG to the detection of “In-flight Icing Potential” in CDOP-3

3 Detection of turbulence related features in sat-images Folie 3 ASII-NG uses pattern recognition methods applied to satellite data combined with NWP forecast fields to detect atmospheric wave features. Required input data are: MSG channels 1,5, 9 and 12 HR-Wind data from PGE09 Cloud top height assignment from PGE03 NWP parameter (u, v, q, potential vorticity, shear)

4 Detection of turbulence related features in sat-images Folie 4 For the detection of wave structures, ASII-NG uses a method which combines wind direction (NWP or PGE09 data) and satellite pixel analysis.

5 Detection of turbulence related features in sat-images Folie 5 At present, the following statistical parameters are used to detect wave features from along-wind pixel excerpts: mean pixel value standard deviation gradient local maxima local minima

6 Detection of turbulence related features in sat-images Folie 6 Tropopause folding Tropopause folding is known in aviation meteorology as being a region affected by turbulence. The algorithm to detect tropopause folding combines satellite and NWP data: tropopause height from pv tropopause height from q water vapour image gradient shear vorticity at 300 hPa wind speed at 300 hPa Logistic regression is applied.

7 Logistic regression Folie 7 First results Logistic regression estimates the probability of an event occurring from so called predictor fields. 30 test cases throughout the year 2014 with a bias on winter and spring cases. The predictand consists of manually drawn tropopause folding zones from NWP fields and water vapour imagery. 5 predictor fields were chosen.

8 Input fields for TP folding Folie 8 Wind shear 300 hPa wind at 300 hPa WV 6.2  m gradient Gradient of tropopause height from PV Gradient of tropopause height from q

9 Logistic regression Folie 9 Our intention is to use the coefficients resulting from the test cases as precept for predicting the probability for being near a tropopause folding for any other satellite image or synoptic situation. Topics being currently investigated: How representative is a chosen predictor field? Is there a variability of the coefficient during the year? How strong is the deviation from case to case? How stable is the output in regard of the predictand?

10 Logistic regression Folie 10 Clear signs of a seasonal trend in the field “tropopause height from q” Winter Spring Summer Fall

11 Logistic regression Folie 11 Gradient of the tropopause height from potential vorticity Winter Spring Summer Fall

12 Logistic regression Folie 12 Water vapour gradient shows a rather constant behaviour along the year.

13 Logistic regression Folie 13 Some parameter show a strong variability over the year.

14 Tropopause folding Folie 14 First results

15 ASII-NG orientation Folie 15 The NWCSAF 2015 Users Workshop should also clarify the orientation ASII-NG will take in the coming years. The development of the precursor product ASII PGE10 (Analysis of Conceptual Models from Satellite Imagery) is frozen. Upon user request, specific conceptual models from ASII PGE10 can be adapted to ASII-NG (e.g. Rapid Cyclogenesis, jet cloud fibers, frontal analysis …) For these conceptual models, this would also mean: Assure their future availability in ASII-NG Maintenance and adaptation to MTG Tuning to foreign satellites

16 Next steps during CDOP Folie 16 Investigate the results from the logistic regression for TP folding and test the input fields on statistical relevance Apply logistic regression to the wave detection module Test other numeric parameters for turbulence detection Prepare ASII-NG for release 2016

17 Folie 17 Thank you for your attention


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