1 / 57

AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2)

AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2). Level 0 to Level 2. Level 0: raw data Level 1A: geolocated radiance in counts Level 1B: calibrated radiance in physical units Level 2: retrieved physical variables

gabe
Download Presentation

AIRS (Atmospheric Infrared Sounder) Regression Retrieval (Level 2)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. AIRS (Atmospheric Infrared Sounder)Regression Retrieval (Level 2)

  2. Level 0 to Level 2 Level 0: raw data Level 1A: geolocated radiance in counts Level 1B: calibrated radiance in physical units Level 2: retrieved physical variables (temperature, humidity and ozone profiles, surface skin temperature, total precipitable water, total ozone content, cloud top height . . .)

  3. 1. Regression Model X= C YT 2. Least squares regression solution C = X Y (YT Y)-1 Y…measurements [nprofs x nchannels] C...Regression coefficients [nlevels x nchannels] X... Atmospheric variables [nlevels x nprofs] Regression Model

  4. 1. Calculate Regression Coefficients C = Xtr Ytr (YtrT Ytr)-1 2. Perform Retrieval (RTV) X= C YT Y… Measurements [nprofs x nchannels] C ... Regression coefficients [nlevels x nchannels] X ... Atmospheric variables [nlevels x nprofs] Subscript tr refers to trainingset Regression Retrieval (1)

  5. 1. Calculate Regression Coefficients C = Xtr Ytr (YtrT Ytr)-1 with Xtr = Xtr – mean(Xtr) Ytr = Ytr – mean(Ytr) 2. Perform Retrieval (RTV) X= C YT orX= mean(Xtr) + CYT with X = X – mean(Xtr) Y = Y – mean(Ytr) Regression Retrieval (2)

  6. M = Cov(Ytr ) U = eig(M) Atr = Ytr U C = Xtr Atr (AtrT Atr)-1 2. Perform Retrieval (RTV) X= mean(Xtr) + C AT with A= Y U, Y = Y – mean(Ytr) M… covariance matrix [nchannels x nchannels] U ... First few eigenvectors of M [nchannels x npc] A ... Projection Coefficients (or amplitudes) [nsamples x npc] Principal Components (PC) Regression Retrieval 1. Calculate Regression Coefficients

  7. The Trainingset Xtr . . . Representative set of atmospheric variables including temperature, moisture, ozone, surface pressure, surface skin temperature and surface skin emissivities Ytr . . . Corresponding set of simulated radiances, calculated by a fast RT (radiative transfer) forward model

  8.  Upwelling IR radiation from surface  Upwelling IR radiation from atm. layers  Reflected downwelling IR radiation  Reflected solar radiation R…radiance, …wavenumber, s…surface, p…pressure, sun…solar, T…temperature, B…Planck function,  …emissivity, …level to space transmittance, ...local solar zenith angle r…reflectivity, with r = (1- )/, *…level to surface (downwelling) transmittance [*= 2(ps)/ (p)] Radiance received by AIRS

  9. Fast Radiative Transfer Forward Model • Fast Model Regression : • - Computation of line-by-line Transmittance  for FM training data set • - Convolve with AIRS SRF (spectral response function) • - Solve regression scheme  = AC for coefficients C using predictors A • (predictors are functions of T, p, absorber amount, scanang …) • Calculate transmittance  for any other profile • Solve RTE to get radiance R

  10. IMAPP AIRS Regression Retrieval Results:Comparison with co-located radiosonde observations (RAOBs)

  11. RAOB – RTV RAOB Residual (Obs – Calc BT) Retrieval Residual (Obs – Calc BT) Co-located RAOB / AIRS single profile retrieval (RTV) residual = observed minus calculated spectrum Temperature (pixel 1259) Brightness Temperature (BT) residual

  12. RAOB – RTV RAOB Residual (Obs – Calc BT) Retrieval Residual (Obs – Calc BT) Co-located RAOB / AIRS single profile retrieval (RTV) residual = observed minus calculated spectrum Humidity (pixel 1516) Brightness Temperature (BT) residual

  13. RMS of RAOB – RTV STDEV of RAOB – RTV Temperature Humidity Co-located RAOB / AIRS retrieval statistics (1899 profiles)

  14. RMS of Residual (Obs – Calc BT) Stdev of Residual (Obs – Calc BT) Mean of Residual (Obs – Calc BT) Co-located RAOB / AIRS retrieval statistics (1899 profiles) Radiosonde Observations AIRS Retrieval

  15. IMAPP AIRS Regression Retrieval Results:Global (240 granules) retrievals and retrievals over the CIMSS direct broadcast area

  16. Globally Retrieved Temperature at 850 mbar Descending Grans Ascending Grans Without Cloudmask With Cloudmask

  17. Globally Retrieved Moisture at 850 mbar Descending Grans Ascending Grans Without Cloudmask With Cloudmask

  18. CIMSS Direct Broadcast area: AIRS measurements (10-23-2003) Brightness Temperature [K] at 1000 cm-1 (no cloudmask)

  19. CIMSS Direct Broadcast area: IMAPP AIRS Retrieval (10-23-2003) Humidity [g/kg] at 700 mbar (no cloudmask) Temperature [K] at 700 mbar (no cloudmask)

  20. Total Precipitable Water [cm] (no cloudmask) Surface Skin Temperature [K] (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval (10-23-2003)

  21. Total Ozone [Dobson Units] (no cloudmask) Ozone [ppmv] at 9.5 mbar (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval (10-23-2003)

  22. Surface Emissivity at 908 cm-1 (no cloudmask) Surface Reflectivity at 2641 cm-1 (no cloudmask) CIMSS Direct Broadcast area: IMAPP AIRS Retrieval (10-23-2003)

  23. IMAPP AIRS Regression Retrieval Results:Comparison with ECMWF analysis fields

  24. AIRS RTV ECMWF ANL Without Cloudmask With Cloudmask AIRS RTV vs. ECMWF Analysis: Temperature at 700 mbar (G192, 09-02-2003)

  25. ECMWF ANL ECMWF – IMAPP RTV IMAPP RTV AIRS RTV ECMWF ANL Pixel 28/49 AIRS RTV vs. ECMWF Analysis: Temperature at 500 mbar and at selected pixel

  26. ECMWF ANL ECMWF – IMAPP RTV IMAPP RTV AIRS RTV ECMWF ANL Pixel 78/53 AIRS RTV vs. ECMWF Analysis: Humidity at 750 mbar and at selected pixel

  27. Scanline 65 AIRS RTV vs. ECMWF Analysis: Humidity along scanline 65 (without cloudmask) IMAPP AIRS Retrieval ECMWF Analysis

  28. Scanline 65 AIRS RTV vs. ECMWF Analysis: Humidity along scanline 65 (with cloudmask) IMAPP AIRS Retrieval ECMWF Analysis

  29. Root Mean Square (RMS) Error Standard Deviation Surface Skin Temperature [K] Total Precipitable Water [cm] RMS and STDEV of ECMWF minus AIRS RTV(G192, 09-02-2003, 4758 clear pixels) Temperature Humidity

  30. RMS of Residual (Obs – Calc BT) Stdev of Residual (Obs – Calc BT) Mean of Residual (Obs – Calc BT) AIRS RTV vs. ECMWF Analysis: Spatial mean of Brightness Temperature (BT) residual IMAPP AIRS Retrieval ECMWF Analysis

  31. AIRS RTV vs. ECMWF Analysis: Spectral mean of Brightness Temperature (BT) residual AIRS RTV ECMWF Analysis Mean Long Wave Mean Short Wave Mean Mid Wave

  32. IMAPP AIRS Regression Retrieval Results:Comparison with ECMWF analysis fields and L2 operational product

  33. IMAPP AIRS RTV ECMWF ANL • Operational AIRS RTV • available on AMSU footprint (=3x3 AIRS FOVs) only • missing areas  retrieval not successful or not validated yet AIRS RTV vs. ECMWF Analysis vs. Operational Product: Temperature at 500 mbar (G192, 04-04-2004)

  34. ECMWF ANL ECMWF – IMAPP RTV IMAPP RTV ECMWF – OP RTV OP RTV Pixel 22/6 AIRS RTV vs. ECMWF Analysis vs. Operational Product: Temperature at selected pixel

  35. IMAPP AIRS RTV ECMWF ANL • Operational AIRS RTV • available on AMSU footprint (=3x3 AIRS FOVs) only • missing areas  retrieval not successful or not validated yet AIRS RTV vs. ECMWF Analysis vs. Operational Product: Humidity at 600 mbar (G192, 04-04-2004)

  36. ECMWF ANL ECMWF – IMAPP RTV IMAPP RTV ECMWF – OP RTV OP RTV Pixel 14/15 AIRS RTV vs. ECMWF Analysis vs. Operational Product: Humidity at selected pixel

  37. IMAPP AIRS Regression Retrieval Results:Comparison with MODIS and GOES retrievals

  38. MODIS RTV vs. AIRS RTV vs. GOES RTV:Temperature and Humidity at 620 mbar (G192, 09-02-2003) Temperature @620 Humidity @620 MODIS RTV AIRS RTV GOES RTV

  39. Accurate atmospheric sounding and cloud variability observations provide an understanding of atmospheric dynamic and cloud-radiation processes that need to be included in climate models. Furthermore they are critical for initializing storm scale models for the prediction of severe weather events. • This requires an algorithm which • fully utilizes the information about the atmospheric state inherent in single field-of-view hyperspectral radiances (e.g. AIRS, IASI, CrIS) • is fast to be useful for real-time applications • provides T, H2O, O3 profiles, as well as surface temperature, surface emissivity, cloud top pressure, cloud optical thickness, CO2 concentration simultaneously under all weather conditions at single-FOV resolution • accounts for the non-linearity between radiances and cloud parameters and atmospheric moisture • accounts for the annual change of CO2 Improved Profile and Cloud Top Height Retrieval by Using Dual RegressionElisabeth Weisz & others

  40. CLEAR CLOUDY Global cloudy training data set 19948 profiles Global training data set (SeeBor V5) 15704 profiles Clear radiance calculations SARTA v1.7 (UMBC) Cloudy radiance calculations Fast RT cloud model (Wei, Yang) Dual Regression Retrieval Algorithm – Part 1 Additional Predictors (spres, solzen) BT, Scanning angle and CO2 Classification Scanning angle, Cloud height and CO2 Classification EOF regression X…atmospheric state, C… regression coefficients, A…compressed radiances (projection coefficients) Clear Regression Coefficients Cloudy Regression Coefficients Regression RTV T, H2O, O3, Tskin, Emissivity, CO2 at single FOV T, H2O, O3, Tskin, CO2, CTOP, COT at single FOV Clear trained EOF regression results Cloud trained EOF regression results

  41. Cloud Height classification (for the cloudy set only): • 8 overlapping classes between 100 and 1000 hPa (200 hPa range each) i.e. 100-300 hPa, 200-400 hPa, ….800-1000 hPa. • CTOP is determined iteratively until the optimal cloud category is selected. • CO2 classification: 5 sets defined by overlapping periods • from 2002 to 2013. Annual mean values from ftp:://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_annmean_gl.txt • or via 370+2*(year-2000).

  42. CLEAR CLOUDY Global cloudy training data set 19948 profiles Global training data set (SeeBor V5) 15704 profiles Clear radiance calculations SARTA v1.7 (UMBC) Cloudy radiance calculations Fast RT cloud model (Wei, Yang) Dual Regression Retrieval Algorithm – Part 1 Additional Predictors (spres, solzen) BT, Scanning angle and CO2 Classification Scanning angle, Cloud height and CO2 Classification EOF regression X…atmospheric state, C… regression coefficients, A…compressed radiances (projection coefficients) Clear Regression Coefficients Cloudy Regression Coefficients Regression RTV T, H2O, O3, Tskin, Emissivity, CO2 at single FOV T, H2O, O3, Tskin, CO2, CTOP, COT at single FOV Clear trained EOF regression results Cloud trained EOF regression results

  43. Clear trained EOF regression results Cloud trained EOF regression results Dual Regression Retrieval Algorithm – Part 2General Idea Cloud Top Altitude Level where Tcloudy-Tclear > 3 K at all levels below that level Final Profile (clear-trained above, cloud-trained below cloud level)

  44. A retrieval (incl. CO2 amount) is generated by using the regression set associated with current year. The CO2 classification is refined if necessary and the retrieval is run again. A first estimate of the CTOP from the clear-trained and cloudy-trained retrieved T profile is obtained. The cloud classification is refined if necessary (i.e. if T derived cloud class differs from cloud-trained solution). If CTOP is close to the surface and the maximum difference between clear and cloudy retrieved Tskin<2.5K the clear-trained solution is used as the as the final at all levels, otherwise the cloud-trained profiles is used. If CTOP≤300mbar the cloud-trained profile is used as the final at all levels. A second (upper level cirrus) cloud top pressure is found if relative humidity exceeds 70% at a level above the cloud altitude derived by the T profile. If maximum difference between clear-trained and cloud-trained T retrieval exceeds 25K the final retrieval below the cloud top is set to missing. Effective cloud emissivity, i.e. (Tskin-Tskin_gdas)/(Tskin_ctop-Tskin_gdas), the cloud condition, and the vertical mean and standard deviation of differences between final T retrieval and GDAS T above and below the cloud top are used to assign quality flags to the final retrieval product. Dual Regression Retrieval Algorithm – Part 2Some details

  45. Retrieval error statistics (simulated) Unstratified (orig) vs. stratified (DR)

  46. CTH comparison with CloudSat and Calipso tropical polar

  47. T and H2O Error Statistics (comparison with radiosondes over CONUS, Dec 2009)

  48. Hurricane Isabel (Sept-13, 2003) Eye MODIS 1km images (1705, 1710) High Dense Cirrus Cirrus Outflow

  49. AIRS Temperature (at 850 hPa) and Humidity (at 700 hPa) . .

  50. AIRS Temperature and Humidity (at 300 hPa)

More Related