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Operational estimation of accumulated precipitation using satellite observation by Eumetsat H-SAF

Operational estimation of accumulated precipitation using satellite observation by Eumetsat H-SAF. Attilio Di Diodato National Centre for Aeronautical Meteorology and Climatology (CNMCA) Italy.

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Operational estimation of accumulated precipitation using satellite observation by Eumetsat H-SAF

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  1. Operational estimation of accumulated precipitation using satellite observation by Eumetsat H-SAF Attilio Di Diodato National Centre for Aeronautical Meteorology and Climatology (CNMCA) Italy

  2. A lot of activities have been running at Centro Nazionale di Meteorologia e Climatologia Aeronautica (C.N.M.C.A.), the Air Force National Weather Centre, to reach the EUMETSAT H-SAF final target: development of algorithms, validation of results, implementation of operative procedure to supply the service and to monitor the service performances. The H-SAF precipitation products generating system is designed with high efficiency, redundancy in machine and data link, easy to control by H24 human operator system.

  3. Italian Air Force - Meteorological Service has made many efforts to rapidly improve satellite receiving system, computing power, licensed software, engineering know-how, consuming nearly nothing of H-SAF budget.

  4. PR-ASS-1 (COSMO-ME domain) HSAF domain

  5. Super-Computing Next implementation of cluster HP with 128 compute nodes each forth-processor Intel Xeon will allow to cover the whole HSAF area with NWP COSMO ME. computing capabilitiesmore than13,5 TFlops.

  6. Basic considerations on time sampling error structure A preliminary study on precipitation time series recorded by a network of 76 automatic stations (perfect sampling time step: 15 minutes) showed the following results:

  7. LEO GEO AUX Basic considerations (2) This time structure of precipitation field implies that instantaneous sampling like that obtained by satellite remote sensing requires accomplishing short time scanning.

  8. H03 PR-OBS-3 Precipitation rate at ground by GEO/IR supported by LEO/MW

  9. Integration of instantaneous precipitation Aim: to obtain a value of accumulated precipitation (RS_acc ) starting from satellite estimation of instantaneous precipitation. RS_acc=∫T RS (t) dt The evaluation of the accumulated precipitation achieved by the integration of any interpolation function (linear, cubic, spline, etc..) is very similar.

  10. Integration of instantaneous precipitation Assumption: rain rate does not change during the 15 minutes intervals. The accumulated precipitation for each time step is obtained with the rain rate value multiplied by the same time step. Total accumulated precipitation in 3, 6, 12 and 24 hours is a sum up of each contribution.

  11. Quality Control Search of outliers every 15 minutes and on the differents accumulation periods, using climatological data (different thresholds by season and geographic position) got from “Climate Atlas of Europe” led by Meteo France inside the project ECSN (European Climate Support Network) of EUMETNET.

  12. PR-OBS 5 Version 1 The algorithm runs on the operational chain at CNMCA; The products are available about fifteen minutes after the synoptic hours; If an input file is missing the algorithm cuts the value of the previous file.

  13. PR-OBS 5 Version 1 The final result contains not negligible random and bias error due to the indirect nature of the relationship between the observation and the precipitation, the inadequate sampling and algorithm imperfections.

  14. PR-OBS 5 Version 1 Use of N-SAF cloud mask

  15. Case study 23 July 2008

  16. Case study 23 July 2008

  17. Case study 23 July 2008

  18. Version2: Use of rain gauge data Ground measurements points (only synoptic stations on GTS network) are used for the intercalibration between satellite estimations and real measurements due to timeliness and cal/val aims.

  19. Use of rain gauge data We have to consider that rain gauge measurements are not perfect, but they are affected by some bias error, due to: • Trace precipitation • Wetting loss • Evaporation loss • Wind-induced error Pc=K(Pg + ΔPw + ΔPe) + ΔPt For operational aims only wind-induced error has taken in account Pc=K * Pg where K = 1/ CR with CR= catch ratio

  20. Use of rain gauge data CR= exp(-0.041* vg) Wherevg = wind speed (m/s) at gauge height. Logarithmic wind reduction equation (Garrat 1992) is used to convert the measured wind speed at certain height to the wind speed at gauge height. vg = vH * log(h/ Z0)/log(H/ Z0) where: • h = Height of the gauge orifice (m) • H = Height of the wind speed measurement (m) (usually 10 m for the stations comply with WMO standards) • Z0 = Roughness length (m) (usually taken as 0.03m) • vH = Wind speed measured at height H

  21. Increments computation • At all rain gauge points the difference between ground measurements and satellite estimated precipitation is calculated. • The values of satellite estimations on the rain gauge points are obtained by interpolation techiques

  22. Version 2: spatial interpolation Distribution of differences over the gridded HSAF area is prepared by using standard kriging method. Use of Kriging method to interpolate the increments

  23. Version 2: final result In each satellite grid point the final product is the sum of satellite precipitation estimation and the increment.

  24. Version 2: case study final result

  25. Problems This method reduces the bias error introduced basically from IR observations by geostationary satellite, but we have other problems: • Ground data are not available over sea areas; • Observation network density is poor over some regions; • Precipitation information inside synop messages are presents only every 6 hours; • We don’t take in account the orography;

  26. Future developments To use the QPF by numerical model COSMO - ME as background field (OI). Model resolution: 7 Km 2 Runs per day (00 and 12 UTC) Output every 3 hours.

  27. Future developments To improve the output of H03 algorithm (istantaneous precipitation), for example through a clouds discrimination.

  28. NEFODINA: a product to discriminate convective clouds An application for automatic detection of convective phenomena (NEFODINA) running operationally at Italian Met Service, which make use of three SEVIRI Channels (6, 7 and 10 ) Algorithms have been upgraded and improved with the contribution of the former Eumetsat fellowship (2003-2005, Dr. Puca) . Early detection of convective clusters and active Nuclei identification. Nowcasting of active nuclei evolving phase.

  29. Running operationally over Mediterranean area.

  30. Under testing over full disk

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