APPLICAZIONI TELERILEVAMENTO IN ATMOSFERA

Transcription

1 APPLICAZIONI TELERILEVAMENTO IN ATMOSFERA

2 ESEMPI Ground-based d radiometry Integrated t V and L Ground-based Gou dbased radiometry Temperature e Profiles Ground-based radiometry Ground-based radiometry Satellite radiometry Satellite radiometry Satellite spectroscopy Humidity Profiles Liquid profiles Temperature Profiles Humidity Profiles Ozone profiles Satellite spectroscopy Tropospheric ozone Satellite spectroscopy Satellite spectroscopy Clouds detection and classif Precipitation cell top altitude

3 Scelta dell input e dell output Generazione dei dtidi dati di addestramento Definizione della topologia Definizione dei coefficienti i via addestramento PROGETTAZIONE Rete Neurale Input Pre- Elaborazione Elaborazione Neurale Post- Elaborazione MODALITÀ OPERATIVA REAL-TIME Output t

4 Estimation of atmospheric variables by microwave radiometry

5 Input vector

6 ANALISI DELLE FUNZIONI PESO = La funzione peso della frequenza osservata viene utilizzata per caratterizzare il modo in cui la trasmissività di un gas cambia in funzione dell altitudine. Per ogni frequenza, la funzione peso di una specie gassosa presenta generalmente un massimo in funzione dell altitudine in corrispondenza del quale si ha il massimo assorbimento atmosferico La scelta delle frequenze per effettuare il sondaggio di una grandezza fisica (come la temperatura o l umidità atmosferica) è realizzata sulla base delle relative funzioni peso, con particolare attenzione alla quota dei massimi e alle relative larghezze.

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8 TEMPERATURA MEDIA RADIATIVA Spessore Ottico = T mr T mr prende il nome di temperatura media radiativa dell atmosfera. Esprime la temperatura globale dell atmosfera come sorgente termica e ci permette di calcolare l opacità tramite

9 Standard approach Coefficients b and c obtained via linear regression analysis

10 GENERAZIONE COPPIE DI ADDESTRAMENTO Radiosoundings collected at various meteorological stations located in Europe Synthetically generated profiles (random fluctuations, thermal inversions,.) Millimeter-wave Propagation Model (LIEBE, 1993) Simulated brightness temperatures as seen from the instrument in the considered radiometric channels (different levels of uncorrelated gaussian noise)

11 SYNTHETICALLY GENERATED ATMOSPHERIC PROFILES Start from the midlatitude summer standard atmosphere For temperature At ground level a random fluctuation with a Gaussian distribution superimposed on its standard value Random fluctuations added to the temperature values at different heights, ht but with a Γ distribution ib ti Ground-based thermal inversions with distributed thickness and strength included For water vapor and liquid water Water t vapor profiles constructed t by adding random irregularities iti with a Gaussian distribution to the standard atmosphere profile. Liquid water simulating fog and/or clouds was generated whenever the relative humidity at a given height was larger than a selected threshold The liquid content of clouds assumed to be proportional to their thickness

12 Vapor standard deviation profile (g/m3)

13 Input: TRAINED NEURAL NETWORK Output: Atmospheric variable(s) T A B1 1 T BN A M no need of a priori information no need of a priori information real time retrieval

14 RISULTATI PER V E L V stand. Dev: = L stand. Dev: = 0.084

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16 PRUNED NETWORK LR: 65 coefficients (two regressions) 48 coefficients

17 Pruning procedure, magnitude based

18 Retrieval of profiles (temperature and water vapor) Profile discretization Choose levels height Reduce dimensionality

19 Comparison is with PCA-LR technique

20 rms error profiles

21 Profiles of rms error of retrieved vapor in case of L > 2 mm. Solid line: nonlinear retrieval and dashed line: linear retrieval

22 Fault tolerance analysis Temperature rms error (K) Undetected failure simulated for each channel of the Undetected failure simulated for each channel of the radiometer (Del Frate and Schiavon, 1998a)

23 Fault tolerance analysis Vapor rms error (g/m3) Undetected failure simulated for each channel of the Undetected failure simulated for each channel of the radiometer (Del Frate and Schiavon, 1998a)

24 Estimation of atmospheric ozone concentrations by satellite spectroscopy

25 GOME- onboard ERS-2 launched 1995 (deorbiting) wavelength range: nm spectral resolution: nm SCIAMACHY- onboard ENVISAT- launched 2002 UV/VIS/NIR range (214 to 2386 nm) spectral resolution: nm OMI- NASA's Aura satellite launched 2004 wavelength range: 270 to 500 nm spectral resolution of about 0.5 nm pixel size is 13 km 24 km at nadir

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28 FINAL TOPOLOGY

29 Simulation issues Climatological standard profiles of air density, pressure and temperature, and ozone, oxygen, water vapor, carbon dioxide and nitrogen dioxide Solar zenith angle value needs to be assumed (besides observation angle) simulate the sensor s s spectral resolution wavelength-dependent noise has to be considered Profile discretization

30 THE INPUT VECTOR Spectral information Geometric and geographic info (solar zenith angle, lat/lon) O 3 slant column by Temperature Independent Differential Absorption Spectroscopy (TIDAS) Everything provided by the sensor EXTENDED PRUNING 1) removal of the weakest connection and retraining 2) removal of unconnected input units Input units with more information content survive

31 Input-ouput pair generation Beyond the model you can use: Direct ground measurements: ozonesondes Other ground measurements: lidar, Brewer Satellite Level 2 products The co-location problem Iapaolo et al, 2007, assume that a satellite and a ground-based measurement are co-located when the centre of the GOME ground pixel is within a region of ±2.5 in latitude and ±5.0 in longitude around the lidar station, and when the time interval between the 2 measurements is less than 12 h

32 RAL profiles Ozone profiles retrieval from GOME Neural Network profiles (Del Frate et al., 2002)

33 Preliminary analysis of the November 1999 ozone mini hole condition over Europe Ozone profiles over this area has been analysed for 3 specific days: November 27: before the mini hole condition December 1: during the mini hole condition December 6: after the mini hole condition

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37 23 NOV 1999 Ozone total column estimated by TIDAS-SAMF method Ozone total column estimated by NN algorithm Map of ozone concentration at 17.4 km of altitude estimated by NN algorithm Map of ozone concentration at 26 km of altitude estimated by NN algorithm

38 26 NOV NOV 1999

39 30 NOV DEC 1999

40 Extended pruning applied to GOME for the design of the best wavelengths for the inversion Model (LIDORT) based EP 5 bands and 194 channels

41 Iapaolo et al, 2007

42 Retrieval of tropospheric ozone from SCIAMACHY Sellitto et al, 2011

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44 Improved performance with VIS

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46 April 2009 Dobson Units

47 August 2009 Dobson Units

48 Retrieval of tropospheric ozone from OMI Sellitto et al, 2011